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WO2018133098A1 - 血管壁应力应变状态获取方法及系统 - Google Patents

血管壁应力应变状态获取方法及系统 Download PDF

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Publication number
WO2018133098A1
WO2018133098A1 PCT/CN2017/072203 CN2017072203W WO2018133098A1 WO 2018133098 A1 WO2018133098 A1 WO 2018133098A1 CN 2017072203 W CN2017072203 W CN 2017072203W WO 2018133098 A1 WO2018133098 A1 WO 2018133098A1
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Prior art keywords
blood vessel
phase
reference point
model
vascular
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Ceased
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PCT/CN2017/072203
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English (en)
French (fr)
Inventor
王洪建
马杰延
任远
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Shanghai United Imaging Healthcare Co Ltd
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Shanghai United Imaging Healthcare Co Ltd
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Priority to PCT/CN2017/072203 priority Critical patent/WO2018133098A1/zh
Priority to US15/638,626 priority patent/US10861158B2/en
Publication of WO2018133098A1 publication Critical patent/WO2018133098A1/zh
Anticipated expiration legal-status Critical
Priority to US17/017,648 priority patent/US11468570B2/en
Ceased legal-status Critical Current

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Definitions

  • the present application relates to a method and system for acquiring a blood vessel wall state, and more particularly to a method and system for reconstructing a blood vessel model based on multi-temporal image data and calculating stress and strain of a blood vessel wall.
  • Imaging plays an important role in the medical field.
  • imaging technologies including, for example, Digital Subtraction Angiography (DSA), Magnetic Resonance Imaging (MRI), and Magnetic Resonance Angiography (MRA).
  • DSA Digital Subtraction Angiography
  • MRI Magnetic Resonance Imaging
  • MRA Magnetic Resonance Angiography
  • CT Computed Tomography
  • CTA Computed Tomography Anniography
  • US Ultrasound Scanning
  • PET Positron Emission Tomography
  • PET single Single-Photon Emission Computerized Tomography
  • SPECT-MR single Single-Photon Emission Computerized Tomography
  • SPECT-MR single Single-Photon Emission Computerized Tomography
  • SPECT-MR single Single-Photon Emission Computerized Tomography
  • SPECT-MR single Single-Photon Emission Computerized Tomography
  • SPECT-MR single Single-Photon Emission Computerized Tomography
  • SPECT-MR single Single-Photon Emission Computerized Tomography
  • SPECT-MR single Single-
  • a three-dimensional model of human tissue and organs can be established on a computer, including the establishment of a blood vessel model.
  • the blood vessels of the human body have different pressures on the blood vessel wall due to different blood flow rates during a cardiac cycle.
  • the vessel wall also produces different internal stress changes during the cardiac cycle. Studying the stress state on the vessel wall can effectively help the doctor to determine which part of the vessel has a potential risk of rupture.
  • Three-dimensional models of blood vessels in the complete cardiac cycle can be established using multi-temporal angiographic data. Using this model to obtain the stress state of the vessel wall helps to accurately diagnose the disease and adjuvant therapy.
  • the method for acquiring a stress-strain state of a blood vessel may include: acquiring first phase phase blood vessel data corresponding to one blood vessel, acquiring second phase phase blood vessel data corresponding to the blood vessel, and establishing a first volume based on the first phase phase blood vessel data a one-phase vascular model, based on the second phase vascular data to establish a second phase vascular model, extracting Depicting a portion of interest in the first phase vascular model, extracting the portion of interest in the second vessel model, and setting a reference point in the portion of interest in the first phase vessel model, The portion of interest in the second phase vascular model finds the reference point, determines a displacement of the reference point, and determines a stress or strain at the reference point based on the displacement of the reference point.
  • the non-transitory computer readable medium can include executable instructions.
  • the instructions when executed by at least one processor, may cause the at least one processor to implement the method of acquiring a stress-strain condition of a vessel wall.
  • the system for acquiring a stress and strain state of a blood vessel wall can include: at least one processor, and the executable instructions.
  • the system can include at least one processor and a memory for storing instructions.
  • operations that may result in the system implementation include the method of acquiring a stress-strain state of a vessel wall.
  • the method of acquiring a stress-strain state of a blood vessel wall may further include comparing the stress or strain with reference data, evaluating a blood vessel state based on the comparison result, and transmitting the blood vessel state evaluation result to a user.
  • the result presentation form in the blood vessel state evaluation result may be at least one of the following forms, including a map, a table, a fixed format text, audio, and the like.
  • the reference data may be stored in one storage device.
  • the transmitting the blood vessel state assessment result to the user may include transmitting the blood vessel state assessment result to the user terminal of the at least one user.
  • the comparing the determined stress or strain with the reference data may include determining a characteristic value of the reference point stress or strain and comparing the characteristic value with the reference data .
  • the characteristic value of the stress or strain of the reference point may include a maximum stress or strain value of the reference point in different phases.
  • the characteristic value of the stress or strain of the reference point may include an average value of stress or strain of the reference point in different phases
  • the portion of interest in the first phase vascular model Subdividing a reference point may include segmenting the blood vessel into a plurality of blood vessel slices, extracting a contour of one of the plurality of blood vessel slices, and setting the reference point based on an outline of the blood vessel slice.
  • the segmenting the blood vessel into a plurality of blood vessel slices may include determining a centerline of the blood vessel, dividing the centerline into a plurality of centerline segments, and determining a segment of the plurality of centerline segments The blood vessel segment corresponding to the line segment is one of the plurality of blood vessel slices.
  • the setting the reference point based on the contour of the blood vessel slice may include: uniformly and equally spacing a certain number of reference points on the blood vessel contour and selecting from the certain number of reference points Reference point.
  • the method of acquiring a stress-strain state of a blood vessel wall may further include selecting an initial point among the set reference points, and counterclockwise or clockwise from the initial point a certain number of reference points are numbered sequentially
  • the first phase vascular model may comprise a cardiac vascular model
  • the cardiac vascular model may comprise a coronary artery and a vein
  • the extracting the first phase vascular model of interest Portions can include automatic extraction of coronary arteries in the cardiac vascular model.
  • the extracting the portion of interest in the first phase vascular model may include: receiving information of two endpoints of the blood vessel from a user and two according to the received blood vessel Information about the endpoint from which the vessel segments between the two endpoints are extracted.
  • the determining the displacement of the reference point may comprise determining a displacement of the reference point between adjacent phases.
  • FIGS. 1A and 1B are diagrams including a vascular state analysis system, according to some embodiments of the present application.
  • 2A is a diagram showing the structure of a computing device that can implement the particular system disclosed in this application, in accordance with some embodiments of the present application;
  • 2B is a block diagram showing the structure of a mobile device that can implement the specific system disclosed in the present application, according to some embodiments of the present application.
  • FIG. 3 is an exemplary block diagram of a processing device shown in accordance with some embodiments of the present application.
  • FIG. 4 is a schematic diagram of a processing module shown in accordance with some embodiments of the present application.
  • FIG. 5 is a schematic illustration of an analysis module shown in accordance with some embodiments of the present application.
  • FIG. 6 is a schematic illustration of a blood vessel extraction unit shown in accordance with some embodiments of the present application.
  • FIG. 7 is a schematic illustration of a blood vessel blocking unit shown in accordance with some embodiments of the present application.
  • Figure 8 is a schematic illustration of a computing unit shown in accordance with some embodiments of the present application.
  • FIG. 9 is an exemplary flow diagram of acquiring a stress and strain state of a blood vessel, according to some embodiments of the present application.
  • FIG. 10 is an exemplary flow diagram of setting a reference point on an extracted vessel model, in accordance with some embodiments of the present application.
  • FIG. 11 is an exemplary flow diagram of segmenting an extracted vascular model into a plurality of vascular slices, in accordance with some embodiments of the present application.
  • FIG. 12 is a schematic illustration of setting a reference point on a blood vessel model, in accordance with some embodiments of the present application.
  • FIG. 13 is a schematic diagram of determining a reference point displacement from different phase vascular models, as shown in some embodiments of the present application;
  • FIG. 14 is an exemplary flow diagram of an output evaluation blood vessel status result, shown in accordance with some embodiments of the present application.
  • modules in a data processing system in accordance with embodiments of the present application, any number of different modules can be used and run on a client connected to the system over a network and / or on the server.
  • the modules are merely illustrative, and different aspects of the systems and methods may use different modules.
  • the imaging system can include one or more modalities.
  • the morphology may include digital subtraction angiography (DSA), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), computed tomography (CT), computed tomography angiography (CTA), ultrasound scanning (US) , positron emission tomography (PET), single photon emission computed tomography (SPECT), SPECT-MR, CT-PET, CE-SPECT, DSA-MR, PET-MR, PET-US, SPECT-US, TMS a combination of one or more of -MR, US-CT, US-MR, X-ray-CT, X-ray-PET, X-ray-US, video-CT, video-US, and/or the like.
  • the target of the imaging scan can be a combination of one or more of an organ, a body, an object, a lesion, a tumor, and the like. In some embodiments, the target of the imaging scan can be a combination of one or more of the head, chest, abdomen, organs, bones, blood vessels, and the like. In some embodiments, the target of the scan can be vascular tissue at one or more locations.
  • the image can be a two-dimensional image and/or a three-dimensional image. In the two-dimensional image, the most subtle The distinguishable element can be a pixel. In a three-dimensional image, the finest resolvable elements can be voxels. In a three-dimensional image, the image can be composed of a series of two-dimensional slices or two-dimensional layers.
  • the image segmentation process can be performed based on the corresponding features of the pixel points (or voxel points) of the image.
  • the respective features of the pixel points (or voxel points) may include a combination of one or more of texture, grayscale, average grayscale, signal strength, color saturation, contrast, brightness, and the like.
  • the spatial location features of the pixel points (or voxel points) may also be used in an image segmentation process.
  • FIG. 1A is a diagram including a vessel state analysis system 100, in accordance with some embodiments of the present application.
  • the vascular state analysis system 100 can include a data collection device 110, a processing device 120, a storage device 130, and an interaction device 140.
  • the data collection device 110, the processing device 120, the storage device 130, and the interaction device 140 can communicate with each other over the network 180.
  • the data collection device 110 can be a device that collects data.
  • the data may include image data, object feature data, and the like.
  • the data collection device 110 can include an imaging device.
  • the imaging device can acquire the image data.
  • the imaging device may be a magnetic resonance imaging (MRI), a computed tomography (CT), a positron emission computed tomography (PET), or a B-mode ultrasound.
  • MRI magnetic resonance imaging
  • CT computed tomography
  • PET positron emission computed tomography
  • B-mode ultrasound a combination of one or more of a b-scan ultrasonography, a diasonography, a thermal texture maps (TTM), a medical electronic endoscope (MEE), and the like.
  • the image data may be a picture or data comprising blood vessels, tissues or organs of the subject.
  • the data collection device can include an object feature collection device.
  • the object feature collection device may collect the heart rate, heart rate, blood pressure, blood flow rate, blood viscosity, cardiac output, myocardial mass, vascular flow resistance, and/or other objects related to blood vessels, tissues, or organs of the subject. Sign the data.
  • the object feature collection device may acquire other object feature data such as age, height, weight, gender, and the like of the object.
  • the image data and object feature data may be multi-temporal data.
  • the multi-temporal data may be data of the same or approximate location on an object obtained at different points in time or phase.
  • the object feature collection device can be integrated in the imaging device to simultaneously acquire image data and object feature data.
  • the data collection device 110 can transmit its collected data to the processing device 120, the storage device 130, and/or the interaction device 140, etc., via the network 180.
  • Processing device 120 can process the data.
  • the data may be data collected by the data collection device 110, data read from the storage device 130, feedback data obtained from the interaction device 140, such as user input data, or from the cloud or external device through the network 180. Data obtained in the middle, etc.
  • the data can include image data, object feature data, user input data, and the like.
  • the processing can include selecting an area of interest in the image data. The area of interest may be selected by the processing device 120 itself or selected based on user input data.
  • the selected region of interest can be a blood vessel, tissue or organ, and the like.
  • the region of interest may be an arterial blood vessel such as a coronary artery, an abdominal artery, a cerebral artery, a lower extremity artery, or the like.
  • Processing device 120 may further segment the region of interest in the image.
  • the method of image segmentation may include edge-based image segmentation methods, such as Perwitt operator method, Sobel operator method, gradient operator method, Kirch operator method, etc., region-based image segmentation methods, such as region growing method, threshold method, Clustering methods and the like, as well as other segmentation methods, such as methods based on fuzzy sets, neural networks, and the like.
  • Processing device 120 may perform model reconstruction on the region of interest.
  • the selection of the model can be based on object feature data, features of the region of interest, and the like. For example, if the region of interest is selected to be a coronary artery, processing device 120 may segment the image containing the coronary artery to extract an image of the coronary artery. Processing device 120 can then reconstruct the model based on subject characteristics, general coronary features, coronary image features, and the like. The reconstructed model may correspond to the shape of the coronary vessel or to the morphology of blood flow in the coronary artery. After establishing a model of the region of interest, processing device 120 can perform the analysis based on the model.
  • processing device 120 may obtain multi-temporal data, such as images of coronary regions of the subject at five different time points.
  • the processing device 120 can Images of regions of interest (eg, entire coronary arteries, branches on the coronary arteries, or blood inlet sections of coronary arteries) of different phases are constructed separately, and the models are analyzed and calculated sequentially.
  • the processing device 120 may perform meshing processing on the models of the different phases and correlate the meshed processed models to reduce the amount of calculation and improve the calculation accuracy.
  • regions of interest eg, entire coronary arteries, branches on the coronary arteries, or blood inlet sections of coronary arteries
  • the processing device 120 may perform meshing processing on the models of the different phases and correlate the meshed processed models to reduce the amount of calculation and improve the calculation accuracy.
  • the results of the analysis and calculations can include physical states and correlation coefficients or parameters of blood vessels, tissues, or organs.
  • the results of analysis and calculation of the coronary artery model may include hemodynamic parameters of the coronary arteries, such as blood flow rate, blood pressure, vessel wall stress, vessel wall shear stress, and blood flow reserve count (Fractional Flow Reserve, A combination of one or more of FFR) and the like.
  • processing device 120 may generate a relationship between the physical state and/or correlation coefficient or parameter and phase or time (eg, changes in hemodynamic parameters over time) based on analysis and calculation results of different phases. . This relationship can be represented by a curve or a comparison table. Based on the curve or look-up table, processing device 120 may obtain the physical state and/or correlation coefficients or parameters of the region of interest of any phase.
  • processing device 120 may perform noise reduction or smoothing on the data or processing results obtained therefrom.
  • processing device 120 may send the data or processing results it obtains to storage device 130 for storage, or to interactive device 140 for display.
  • the processing result may be an intermediate result generated during the processing, such as a model of the region of interest, or may be the final result of the processing, such as analysis and calculation of hemodynamic parameters.
  • processing device 120 may be one or more processing elements or devices, such as a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (digital signal) Processor, DSP), system on a chip (SoC), microcontroller (mCU), etc.
  • processing device 120 may also be a specially designed processing element or device having special functions. Processing device 120 may be local or remote relative to data collection device 110.
  • the storage device 130 can store data or information.
  • the data or information may include data acquired by the data collection device 110, processing results or control instructions generated by the processing device 120, user input data received by the interaction device 140, and the like.
  • the storage device 130 can be one or more storage media that can be read or written, including a static random access memory (static random access memory, SRAM), random-access memory (RAM), read-only memory (ROM), hard disk, flash memory, and the like.
  • storage device 130 can also be a remote storage such as a cloud disk or the like.
  • the interaction device 140 can receive, transmit, and/or display data or information.
  • the received data or information may include data acquired by the data collection device 110, processing results generated by the processing device 120, data stored by the storage device 130, and the like.
  • the data or information displayed by the interaction device 140 can include the actual image 150 of the cardiovascular obtained by the data collection device 110, the cardiovascular model 160 established by the processing device 120 from the actual image 150, and the processing device 120 from the cardiovascular model 160.
  • Display forms can include 2D or 3D medical images, geometric models and their mesh analysis, vector graphics (such as velocity vector lines), contour maps, filled contour maps (cloud maps), XY scatter plots One or more combinations of particle trajectory maps, simulated flow effects, and the like.
  • the data or information transmitted by the interaction device 140 can include input information of the user.
  • the interaction device 140 can receive one or more operational parameters of the processing device 120 input by the user and send to the processing device 120.
  • the interaction device 140 can include a user interaction interface.
  • the user can input a user input data to the interactive device 140 via a particular interactive device, such as a mouse, keyboard, touch pad, microphone, or the like.
  • a particular interactive device such as a mouse, keyboard, touch pad, microphone, or the like.
  • the user can click on the model displayed by the interactive device 140 and select the region of interest in the model.
  • the user can select any location in the blood vessel model displayed by the interaction device 140, and the interaction device 140 can retrieve and display the blood flow rate, blood pressure, blood flow, etc. at the location from the processing device 120.
  • the interaction device 140 can be a display device or the like having a display function. In some embodiments, the interaction device 140 can have the functionality of some or all of the processing device 120. For example, the interaction device 140 can perform operations such as smoothing, noise reduction, color change, and the like on the results generated by the processing device 120. For example, the color change operation can change a grayscale image into a color image or turn a color image into a grayscale image.
  • the interaction device 140 and the processing device 120 can be an integrated device. The integrated device can implement the functions of the processing device 120 and the interaction device 140 at the same time.
  • interactive device 140 can include a desktop computer, a server, a mobile device, and the like.
  • Mobile devices may include laptops, tablets, ipads, built-in devices of vehicles (eg, motor vehicles, boats, airplanes, etc.), wearable devices, and the like.
  • the interaction Device 140 may include or be connected to a display device, printer, fax, or the like.
  • the network 180 can be used for communication within the vascular status analysis system 100, receiving information external to the system, transmitting information to the outside of the system, and the like.
  • the network 180 can be accessed between the data collection device 110, the processing device 120, and the interaction device 140 by way of a wired connection, a wireless connection, or a combination thereof.
  • Network 180 can be a single network or a combination of multiple networks.
  • network 180 may include, but is not limited to, a combination of one or more of a local area network, a wide area network, a public network, a private network, a wireless local area network, a virtual network, a metropolitan area network, a public switched telephone network, and the like.
  • network 180 may include a variety of network access points, such as wired or wireless access points, base stations, or network switching points, through which the data sources connect to network 180 and transmit information over the network.
  • FIG. 1B Another schematic of a vascular state analysis system 100 is shown in FIG. 1B.
  • Figure 1B is similar to Figure 1A.
  • the processing device 120 can be directly connected to the data collection device 110, and the data collection device 110 is not directly connected to the network 180.
  • the data collection device 110, the processing device 120, and the interaction device 140 may directly exchange data or information without using the network 180.
  • these devices can also exchange data or information in a removable memory or other intermediary.
  • FIG. 2A is a diagram showing the structure of a computing device 200, in accordance with some embodiments of the present application.
  • the computing device 200 can implement the particular system disclosed in this application.
  • the particular system in this embodiment utilizes a functional block diagram to explain a hardware platform that includes a user interface.
  • Computing device 200 can implement one or more components, modules, units, sub-units (e.g., processing device 120, interaction device 140, etc.) that are currently described in vessel state analysis system 100. Additionally, one or more components, modules, units, sub-units (eg, processing device 120, interaction device 140, etc.) in vessel state analysis system 100 can be utilized by computing device 200 through its hardware devices, software programs, firmware, and The combination is implemented.
  • Such a computer can be a general purpose computer or a computer with a specific purpose. Both computers can be used to implement the particular system in this embodiment. For the sake of convenience, only one computing device is drawn in FIG. 2A, but the description is performed in this embodiment.
  • the related computer functions of information processing and pushing information can be implemented in a distributed manner by a similar set of platforms, decentralizing the processing load of the system.
  • computing device 200 can include an internal communication bus 210, a processor 220, a read only memory (ROM) 230, a random access memory (RAM) 240, a communication port 250, and an input/output component 260, Hard disk 270, user interface 280.
  • the internal communication bus 210 can enable data communication between components of the computing device 200.
  • the processor 220 can execute program instructions to perform one or more of the functions, components, modules, units, subunits of the vascular state analysis system 100 described in this disclosure.
  • Processor 220 is comprised of one or more processors.
  • Communication port 250 can be configured to enable data communication (e.g., via network 180) between computing device 200 and other components of vascular status analysis system 100, such as data collection device 110.
  • Computing device 200 can also include various forms of program storage units and data storage units, such as hard disk 270, read only memory (ROM) 230, random access memory (RAM) 240, which can be used for computer processing and/or communication.
  • Input/output component 260 supports input/output data flow between computing device 200 and other components, such as user interface 280, and/or with other components of blood vessel state analysis system 100, such as database 140.
  • Computing device 200 can also transmit and receive information and data from network 180 via communication port 250.
  • the user device for displaying and interacting with location related information is a mobile device 300.
  • Mobile device 300 may include a smartphone, tablet, music player, portable game console, global positioning system (GPS) receiver, wearable computing device (eg, glasses, watches, etc.), or other forms.
  • the mobile device 300 in this example includes one or more central processing units (CPUs) 204, one or more graphics processing units (GPUs) 203, a display 202, a memory 206, and an antenna 201, such as A wireless communication unit, a storage unit 209, and one or more input output (I/O) devices 205.
  • CPUs central processing units
  • GPUs graphics processing units
  • any other suitable components including but not limited to a system bus or controller (not shown), may also be included in the mobile device 300.
  • a mobile operating system 207 such as iOS, Android, Windows Phone, etc.
  • Application 208 can be loaded into memory 206 from storage unit 209 and executed by central processor 204.
  • Application 208 may include a browser or other device suitable for receiving and processing images or vascular status analysis on mobile device 300.
  • Mobile app The interaction of the user with one or more components of the vessel state analysis system 100 with respect to image or vessel state analysis related information may be obtained by the input/output system device 205 and provided to the processing device 120, and/or other in the vessel state analysis system 100. Components, for example: through the network 180.
  • the processing device 120 can include a receiving module 310, a processing module 320, a storage module 330, and an analysis module 340 and an output module 350.
  • the receiving module 310 can acquire image data, object feature data, and the like from the data collection device 310 and/or the storage device 330.
  • the image data may be a picture or data comprising blood vessels, tissues or organs of the subject.
  • the subject feature data may include heart rate, heart rate, blood pressure, blood flow rate, blood viscosity, cardiac output, myocardial mass, vascular flow resistance, and other object characteristic data related to blood vessels, tissues or organs, and subject age of the subject. Other object characteristics such as height, weight, gender, etc.
  • the image data and object feature data may be multi-temporal data.
  • the multi-temporal data may be data of the same or approximate location on an object obtained at different points in time or phase.
  • the processing module 320 can perform correlation processing of data and construct corresponding images according to the corresponding data.
  • the data may be from the receiving module 310, the storage module 330, and/or other modules not shown.
  • the data may be external data resources obtained over the network 180.
  • the data processed by processing module 320 may be data relating to a particular portion of the target object, such as a combination of one or more of the brain, heart, blood vessels, lungs, bronchi, and the like.
  • processing module 320 can process blood vessel related data.
  • the processing module 320 can process the data based on a plurality of modes, including a combination of one or more of a mode of selecting data parameters, an automatic processing mode, a program processing mode, a function processing mode, and the like.
  • the user may select data that needs to be processed, for example, may select to treat a blood vessel at a particular location in the image.
  • the function processing mode may be a combination of one or more of blood vessel image data preprocessing based on histogram fitting, image data processing based on function transformation, image data processing based on weight calculation, and the like.
  • the processing of the image data may include image pre-processing, image coarse segmentation, image feature point tracking, and/or image transformation, and the like.
  • different program processing methods can be selected to perform data processing for different requirements at different stages.
  • the function processing mode may include various types of function processing methods, including a level set function method, One or more combinations of gradient descent method, exponential function transform method, histogram data expansion function fitting, and the like.
  • the storage module 330 can store data or information.
  • the stored data or information may be in various forms, such as a combination of one or more of numerical values, signals, images, related information of a target object, commands, algorithms, programs, and the like.
  • the stored data may be a blood vessel image, a blood vessel image parameter, blood vessel image processed data, or a program and/or algorithm applied by blood vessel image processing, or the like.
  • Analysis module 340 can perform data analysis.
  • the analysis module can analyze the calculation results of the processing module 320. For example, the calculation result of the analysis module is compared with a reference value.
  • the analysis module 340 can also format the results of the analysis. For example, plot the results of the analysis in the form of a chart or report.
  • the output module 350 can output the generated analysis result or calculation data.
  • the output module 350 can send the analysis results or calculation data to the storage device 130 for storage, or to the interactive device 140 for display or otherwise presented to the client (eg, sound, etc.).
  • the calculation result may be an intermediate result generated, such as a model of the region of interest, or a final result generated, such as a analyzed and calculated vascular wall stress-strain parameter or a relationship curve between the calculated result and the time phase, or a comparison table.
  • the processing module can include a data loading unit 410, a blood vessel extraction unit 420, a blood vessel blocking unit 430, a blood vessel matching unit 440, and a computing unit 450.
  • the modules shown can be connected directly (and/or indirectly) to each other.
  • Data loading unit 410 can load blood vessel data to establish a blood vessel model.
  • the blood vessel data may be from the receiving module 310 and/or the storage module 330.
  • the blood vessel data can be from the receiving module 310.
  • the data of the receiving module 310 is from the data collecting device 110.
  • data acquisition device 110 produces a series of CT tomograms.
  • the series of CT tomograms can be transmitted to the data loading unit 410 via the receiving module 310.
  • the blood vessel data can be from storage module 330.
  • the case data of a certain object is stored in the storage module 330.
  • the contrast picture of the related diagnosis of the object needs to be retrieved, the data can be directly read from the storage module 330 and transmitted to the data loading unit 410.
  • the data loading unit 410 can load the acquired blood vessel image data into the data processing module 320.
  • the vascular image data may be a series of angiographic images, or some electronic data reflecting the vascular structure.
  • a series of angiographic images may be images of different sections of a blood vessel or a portion of a blood vessel.
  • the number of the series of angiographic images may be determined by the processing capabilities of the data acquisition device 110 or the amount of information stored in the storage module 330 for the portion of the blood vessels. For example, if the imaging capability of a data acquisition device 110 can be x sections of a certain blood vessel, the number of acquired angiographic images can be x or less than x.
  • the acquired series of angiographic images may be y or less than y.
  • the vascular image data can be a series of electronic data.
  • the data output form of a certain data collection device 110 is a simulated blood vessel model, and the blood vessel model is composed of several voxels, and each voxel may have a corresponding coordinate. The sum of the coordinates of each voxel can be used to reconstruct a complete vascular model.
  • the coordinate information of the voxel can be transmitted as electronic data and loaded by the data loading unit 410.
  • the amount of data of the electronic data may be determined by the resolution of the voxel. For example, if the vascular model is refined into more voxels, a larger amount of data may be generated.
  • the loaded blood vessel image data may be image data corresponding to a blood vessel state of a certain phase, image data corresponding to a blood vessel state of a plurality of different phases, or dynamic image data of a blood vessel state within a certain period of time.
  • the loaded vascular image data is a vascular state corresponding to the image data for a certain phase.
  • the phase may be the phase in which the heart contracts to a minimum, the phase in which the heart is dilated to the maximum, or a phase in which the heart contracts or relaxes.
  • the loaded vascular image data may be image data corresponding to a plurality of sets of vascular states of different phases.
  • the plurality of sets of different phases may constitute a partial or complete cardiac cycle.
  • the part of the cardiac cycle may be a time period corresponding to a diastolic process, or a time period corresponding to a cardiac contraction process.
  • the vascular state corresponding image data of the plurality of different phases may reflect the state change of the partial blood vessel within the corresponding time period.
  • the number of groups of the different sets of different phases may be determined by the capabilities of the data collection device 110, by the processing capabilities of the load unit, or by the user. For example, if the imaging capability of a certain data acquisition device 110 is higher in the time domain, more sets of blood vessel images can be generated during the studied time period.
  • the number of sets of blood vessel image data sets loaded may be limited by the processing capability of the data loading unit 410.
  • the precision of a vascular state study is very high, it needs to be Multiple sets of vascular imaging data were sufficient to restore changes in vascular status during the study period.
  • the vascular image data may also be vascular dynamic image data for a certain period of time.
  • the data output result of a data collection device 110 can be a dynamic movie. The dynamic film can play a continuous state change of the blood vessel during the study period in a certain number of frames.
  • the vascular image data may be an angiographically enhanced image.
  • the image output by the data acquisition device 110 may include blood vessels and other body tissues.
  • the other body tissues may include muscle tissue, bones, body organs, and the like.
  • the blood vessels and other body tissues may cross each other.
  • an angiographic enhancement process can be performed on the output image of the data acquisition device 110 to detach portions of the blood vessel.
  • the loaded vascular image data can be used to construct a blood vessel model.
  • the blood vessel model can be a three-dimensional blood vessel model. If several sets of vascular images of different phases are loaded, several sets of three-dimensional vascular models of different phases can be constructed.
  • the model can be constructed from a series of vessel cross-sectional images or constructed from vascular electronic data.
  • the loaded data is a series of images of different cross-sections of blood vessels, and the blood vessel sections can be superimposed and fitted into a complete space according to the contours of the blood vessels in the respective sections and the coordinates corresponding to the sections.
  • Vascular model In some embodiments, the construction of the vascular model is constructed from electronic data.
  • the loaded blood vessel data is the coordinates of different voxels on the blood vessels output by the data acquisition device 110, and the blood vessel model establishing process may be to arrange the coordinates of these voxels in space and fit into a complete blood vessel. model.
  • the blood vessel model can be a dynamic three-dimensional image.
  • the data output by the data acquisition device 110 is continuous change information of a certain voxel coordinate on a blood vessel for a certain period of time.
  • the vascular model establishment process may be to fit and continuously play each voxel coordinate to generate a continuous three-dimensional image model with a certain number of frames.
  • the blood vessel extraction unit 420 can extract a certain portion of the blood vessel in the blood vessel model.
  • the portion of the blood vessel can be a cluster of blood vessels, a blood vessel or a segment of a blood vessel.
  • the subject of the contrast is a blood vessel of the heart.
  • the blood vessels of the heart can be divided into coronary arteries (coronary arteries) and veins. For the different symptoms of the subject, the doctor may judge the subject's coronary artery or vein lesions, then the coronary veins and veins in the established blood vessel model need to be stripped to facilitate the doctor's diagnosis.
  • the doctor has preliminarily determined that a certain blood vessel may have an abnormality in the output of the data collection device 110, and after the blood vessel model is established, the blood vessel extraction unit 420 may be utilized to generate the lesion.
  • the blood vessels are extracted for specific analysis.
  • the partial blood vessel segment can be extracted for specific analysis.
  • the blood vessel blocking unit 430 can divide the blood vessels.
  • the established vascular model or extracted partial vascular model needs to be segmented into small vascular units (also referred to as vascular slices) prior to further analysis.
  • small vascular units also referred to as vascular slices
  • the blood vessel blocking unit 430 can divide the blood vessel wall into a plurality of segment curved surfaces along the direction of blood vessel extension.
  • the vascular segmentation unit 430 can segment the vessel segment under study into a plurality of hollow cylindrical segments perpendicular to the vessel centerline.
  • the blood vessel centerline is an imaginary line at the center of the blood vessel along the direction in which the blood vessel extends.
  • the hollow portion of the hollow cylindrical section is the portion through which blood flows.
  • the segments may be uniform or non-uniform. In some embodiments, a uniform division of the entire vascular model to be studied can be performed. In some embodiments, where a portion of the blood vessel may be a more interesting portion, the portion of the blood vessel may be relatively finely segmented (eg, into relatively small or shorter pieces or blood vessel slices), while other relative times Important part of the blood vessel can be relatively rough (eg, segmented into relatively large or long pieces or blood vessel slices).
  • the vessel matching unit 440 can match the same segment of blood vessels of different phases.
  • the loading processing module 320 is image data of the same segment of blood vessels of several sets of different phases. After the establishment of the vascular model, if the state of the same portion of the vessel at different phases is to be studied, it is necessary to match the different phase vascular models to find the vessels on the vessel of different phase vessels.
  • the matching can be to match a certain blood vessel or to match a point on a blood vessel. For example, voxels on blood vessels are numbered when a blood vessel model is established, and regions corresponding to the same numbered voxels can be considered as matching regions after generating different phase phase blood vessel models.
  • the calculation unit 450 can be used to calculate a state change of the blood vessel.
  • the change in state of the blood vessel may include displacement of a point on the blood vessel, strain and/or stress changes of the blood vessel over a period of time, strain and/or stress distribution of the blood vessel at a certain phase, and the like.
  • the calculation unit 450 can separately calculate the state of the blood vessel slice described above, and can re-integrate the calculated result according to the cutting algorithm of the blood vessel slice to obtain the state of the entire blood vessel.
  • the computing unit 450 can calculate the vascular model in pairs, respectively, to derive the calculated changes in the two blood vessels in the corresponding state.
  • the pairwise calculation refers to calculating the adjacent two phase (also referred to as "phase") vascular models.
  • the change in the state of the reference point For example, the changes in the reference points on the first phase and the second phase vascular model are calculated, and the changes in the reference points on the second phase and the third phase vascular model are calculated.
  • the calculating unit 450 can also integrate the two-two calculated vascular state changes according to the phase sequence to obtain continuous vascular state changes. For example, by integrating the reference point change of the first phase to the second phase with the reference point change of the second phase to the third phase, the first phase to the third phase can be obtained.
  • the dynamic change of the reference point By analogy, the dynamic changes of each reference point on the vascular model in the complete cardiac cycle can be obtained.
  • the processing module 320 is merely for convenience of description, and the present application is not limited to the scope of the embodiments. It will be understood that, after understanding the principle of the system, it is possible for the various modules to be combined arbitrarily or the subsystems are connected to other modules without being deviated from the principle. Various modifications and changes in the form and details of the application of the method and system.
  • the vascular model building function of data loading unit 410 can be implemented by a vascular model building subunit.
  • the blood vessel extraction unit 420 and the blood vessel segmentation unit 430 can be combined into one unit that obtains a blood vessel slice.
  • FIG. 5 is a schematic diagram of an analysis module 230, shown in accordance with some embodiments of the present application.
  • the analysis module 340 can include a calculation result comparison unit 510, a result generation unit 520, and a result transmission sub-unit 530.
  • the individual units shown may be directly (and/or) indirectly connected to each other.
  • the calculation result comparison unit 510 can compare the calculation result of the blood vessel state by the calculation unit 450 with a reference result and generate a comparison result.
  • the reference result may be data stored in the storage module 330, may be data stored in the network 180, or data input by the user.
  • the vascular state reference results of the study and related alignment conclusions can be stored in a table.
  • the reference result may be a correspondence relationship between a blood vessel stress range and a dangerous degree. The degree of danger can be divided into normal, early warning, dangerous, extreme danger and the like.
  • the user can manually enter the correspondence based on clinical experience.
  • the comparison can be a comparison of calculated results of vascular status for different periods of the same subject. For example, the calculation result of the blood vessel state of the object in the morning, the calculation result of the blood vessel state at noon and the calculation result of the evening can be compared by the calculation result comparison unit 510 and the state change of the blood vessel of the object during the day can be obtained, which can help the doctor understand the object. When is the day when the blood vessels are more likely to be dangerous.
  • the result generation unit 520 can generate a different form of presentation manner from the conclusions obtained by the comparison result unit 510.
  • the presentation manner may be a statistical graph, a statistical table, a fixed format document and audio, and the like.
  • the result presentation can be presented to the user via the interaction device 140.
  • the result generating unit 520 may include a graph generating subunit 521, a table generating subunit 522, a text report generating subunit 523, an audio report generating subunit 524, and a video report generating subunit 525.
  • the individual units may be connected directly (and/or indirectly) to each other.
  • the graph generation sub-unit 521 can generate a series of statistical graphs, or a blood vessel model map of color and/or gray scale, and the like.
  • the chart may include a graph such as a graph, a line graph, a pie chart, and a histogram.
  • the graph may be a graph of stress changes in a blood vessel during a cardiac cycle.
  • the color and/or grayscale blood vessel model map may use color shades and/or gray scales to indicate the distribution of state parameters on a segment of the blood vessel. For example, a darker color can be used to indicate a portion of a blood vessel that is subjected to greater stress. This part can be considered as part of the risk of rupture.
  • Table generation sub-unit 522 can generate a series of data comparison tables.
  • the column item of the table may be the number of a series of blood vessels or the number of a blood vessel slice
  • the line item may be a type of state parameter of a blood vessel or a blood vessel slice, such as displacement, strain, stress, and the like.
  • the reference value corresponding to the state parameter type can also be displayed in the table and used as a basis for giving an early warning conclusion of the current blood vessel or blood vessel slice.
  • the early warning conclusions can be normal, early warning, dangerous and extremely dangerous.
  • the text report generation sub-unit 523 can generate a textual description in a certain format from the conclusions obtained by the calculation result comparison unit 510 and the calculation result of the calculation unit 450.
  • the textual description in a certain format may be pre-designed text.
  • the text is filled or modified in some fixed position based on the calculation results and the comparison conclusions.
  • the pre-designed text format can be "...the blood vessel...the maximum strain in a cardiac cycle is..., the maximum stress is..., the reference normal maximum stress of the blood vessel is..., the risk of rupture of the blood vessel Lower / higher / very high.
  • the audio report generation sub-unit 524 can generate a voice report based on the comparison conclusion and/or issue a corresponding alert sound.
  • the voice report may be a comparison result and a calculation result added to a corresponding portion of the pre-recorded voice file.
  • the text format content described above is recorded as a voice template, and the comparison result or the calculation result is broadcast at a specific location.
  • the alert sound may be issued based on an early warning conclusion in the comparison result. For example, when the early warning conclusion is dangerous or extremely dangerous, the interaction device 140 can emit beeps at different frequencies to alert the user.
  • the video report generation sub-unit 525 can generate an animation or video based on the conclusions obtained by the calculation result comparison unit 510 and the calculation results of the calculation unit 450.
  • video report generation sub-unit 525 can generate an animation of the calculations of computing unit 450, showing changes in blood vessels, blood flow, or vascular status over a period of time (eg, within one or more cardiac cycles).
  • a video report can include audio, text, icons.
  • an animation showing changes in blood vessels, blood flow, or blood vessel status over a period of time can be combined with an audio presentation, or textual description, to provide an animation at different times.
  • One or more parameters of blood vessels, blood flow, or vascular status eg, blood pressure, stress, etc.).
  • the different result presentation manners that the result transmission sub-unit 530 can generate are transmitted to the transmission module 350, and then transmitted to different user terminals, or directly to different user terminals.
  • the results generated by the result generation unit 520, the calculation results of the calculation unit 450, and the like may be transmitted to the user's terminal via the network 180.
  • the user terminal may be a computer, a mobile phone, a display device, a printer, a fax, or the like.
  • the results can be transmitted simultaneously to multiple user terminals.
  • multiple terminals may be local or remote with respect to data collection device 110 or processing device 120. For example, a certain subject has a plurality of consultation doctors whose blood vessel status results can be simultaneously transmitted to the terminal of the subject, the family member, the guardian, and the plurality of consultation doctors.
  • analysis module 340 is merely for convenience of description, and the present application is not limited to the scope of the embodiments. It will be understood that, after understanding the principle of the system, it is possible for the various modules to be combined arbitrarily or the subsystems are connected to other modules without being deviated from the principle. Various modifications and changes in the form and details of the application of the method and system. For example, in some embodiments, a portion of the sub-units in result generation unit 520 may be omitted.
  • FIG. 6 is a schematic illustration of a blood vessel extraction unit 420, shown in accordance with some embodiments of the present application.
  • the blood vessel extraction unit 420 may include a blood vessel automatic segmentation subunit 610 and a blood vessel semi-automatic segmentation subunit 620.
  • the segmentation refers to segmenting the constructed blood vessel model, and may include dividing the blood vessel with other tissues of the human body, dividing a certain type of blood vessel (such as a coronary artery) with other kinds of blood vessels, and separating a certain part of the blood vessel with other parts. Perform segmentation (such as dividing a part of a blood vessel close to the heart). Vessel segmentation can be based on one or more algorithms.
  • the blood vessel segmentation algorithm may include a threshold method, a region growing method, an energy function based method, a level set method, a region segmentation and/or merging, an edge tracking segmentation method, Statistical pattern recognition method, mean clustering segmentation method, model method, segmentation method based on deformable model, artificial neural network method, minimum path segmentation method, tracking method, rule-based segmentation method, coupled surface segmentation method, etc., or the above segmentation Any combination of methods.
  • the blood vessel automatic segmentation sub-unit 610 can automatically perform the segmentation process on the constructed blood vessel model.
  • the automatic segmentation algorithm may be pre-stored within a computing device, such as the computing device described in FIG. 2A. For example, for a certain type of vascular analysis, each blood vessel in a cluster needs to be analyzed separately, and a single blood vessel in the model can be automatically segmented after the blood vessel model is constructed.
  • the automatic segmentation algorithm may be determined based on the location of the imaged blood vessel. For example, during the diagnosis of a heart disease subject, the doctor may wish to know the stress distribution of the subject's coronary artery.
  • the coronary artery and the vein in the blood vessel model can be automatically segmented.
  • the automatic segmentation algorithm may be determined based on the purpose of the diagnosis. For example, in some disease diagnoses, a physician may need to know the distribution of capillaries, and for a constructed vessel model, a relatively thicker vessel portion and a relatively thin vessel portion may be segmented.
  • the automatic segmentation algorithm may be stored in the storage module 330 and may be selected by the user through the interaction device 140 when the call is needed.
  • the automatic segmentation algorithm can also be upgraded and optimized through the network 180. For example, device manufacturers may optimize the method of vessel segmentation or provide new methods of vessel segmentation on a regular or irregular basis, and provide relevant data packages for users to download for updates.
  • the vessel semi-automatic segmentation sub-unit 620 can accept user commands to assist in vessel segmentation.
  • the physician may perform a specialized vessel segmentation procedure based on certain particular case requirements, and the vessel segmentation procedure may not be or is not optimally achieved by automated segmentation.
  • the doctor can manually divide according to the specific symptoms of the subject. For example, the physician can select a portion of the constructed vascular model that is more likely to have a lesion to be segmented by the interaction device 140. The selection may be to specify a certain area on the blood vessel model, or a certain section of blood vessels.
  • the designation of a certain segment of blood vessel may be by manually selecting two points on a blood vessel to divide the blood vessel between the two points.
  • the manual segmentation may also be based on automatic segmentation.
  • the coronary artery can be first segmented by the autovascular segmentation subunit 610, and the portion of the coronary artery that may be lesioned by the doctor is further divided.
  • the automatically segmented blood vessels may have some errors due to insufficient optimization of the automatic segmentation algorithm, or insufficient computational power of the computing device, and these errors may affect the diagnosis of the disease by the doctor, and the manual method is required. Automatically segmented blood vessels Make modifications.
  • the vein can be manually segmented by the blood vessel semi-automatic segmentation unit 620.
  • the computing device can determine that the capillary threshold is adjusted to be wider to avoid or reduce leakage of a portion of the capillaries.
  • the portion of the thicker blood vessel can be segmented by the blood vessel semi-automatic segmentation subunit 620.
  • FIG. 7 is a schematic illustration of a blood vessel blocking unit 430, shown in accordance with some embodiments of the present application.
  • the blood vessel blocking unit 430 may include a centerline extraction subunit 710 and a blood vessel delineator stator unit 720.
  • the blood vessel segmentation may be a unit that divides a blood vessel into a number of segments that can be numerically analyzed. For example, a blood vessel model is divided into sections according to the number of voxels. As another example, the blood vessel is cut in a direction perpendicular to the centerline of the blood vessel and divided into a plurality of blood vessel segments. For example, a blood vessel wall or the like corresponding to a certain surface area is cut on the blood vessel wall with a certain size.
  • the centerline extraction sub-unit 710 can extract the centerline of a certain segment of the blood vessel.
  • the vessel centerline may refer to an imaginary line located along the vessel's direction inside the vessel.
  • the blood vessel centerline can include a collection of one or more pixel points (or voxel points) in the blood vessel.
  • a blood vessel can include a collection of pixel points (or voxel points) within a blood vessel boundary line and a blood vessel boundary line.
  • the vessel centerline may comprise a collection or a set of lines of pixel points (or voxel points) near the center of the blood vessel.
  • the vessel centerline can include one or more vessel endpoints. The blood vessel centerline is a path between the endpoints.
  • the method of determining the centerline of the blood vessel may be to cut the blood vessel in parallel into a number of blood vessel segments, and determine a center point of each blood vessel center such that the statistical variance of the point from the blood vessel wall is minimized.
  • the center points of the individual vessel segments were fitted with curves. If the number of segments of the vessel being cut is sufficient, the fitted curve can be considered to be the centerline of the segment of the vessel.
  • an exemplary method for vascular centerline extraction may be referred to the International Application No. PCT/CN2016/097294, filed on Aug.
  • the center line of the segment of the blood vessel After obtaining the center line of the segment of the blood vessel, it can be equally divided into a plurality of segment center line segments such that each segment of the center line segment corresponds to a segment of the blood vessel wall.
  • This part of the blood vessel wall is used as a blood vessel slice outputted by the blood vessel segmentation module 430.
  • the center line segment is selected sufficiently small, the thickness of the blood vessel slice along the blood vessel direction may be disregarded, and the blood vessel slice is a ring structure considering the shape of the blood vessel cross section.
  • the blood vessel delineator stator unit 720 can discretize the blood vessel slices.
  • the contour of the blood vessel The target stator unit 720 can determine the contour of the blood vessel slice. Since the blood vessel wall of the blood vessel slice has a certain thickness, the contour of the blood vessel wall can be approximated as the contour of the inner wall of the blood vessel, the contour of the outer wall of the blood vessel, or other closed curve which can describe the annular structure of the blood vessel slice. In some embodiments, since the contour of the blood vessel slice is a continuous curve, in order to facilitate the analysis, the contour curve needs to be discretized. For example, a reference point is set at intervals of the blood vessel contour curve. The distribution of the reference points may be uniform or non-uniform.
  • the study of cardiac blood vessels may focus primarily on the state of the blood vessel near the side of the myocardium, then more reference points may be placed on one side of the blood vessel slice near the myocardium, while less on the other side. Reference point.
  • the reference points can be calibrated to perform matching of the same vessel segment at the same time. For example, marking a point on a blood vessel slice as "M-n" indicates that the point is the nth reference point on the Mth blood vessel slice. When analyzing the same vessel segment state at different times, the reference points of the same marker can be matched.
  • the marking method of the reference point may be marked according to the coordinates of the space in which the blood vessel model is located, for example, marking at a distance from the origin, and the like.
  • the marking of the reference point can also be made according to the characteristics of the blood vessel type. For example, when studying a blood vessel covering a surface of a myocardium, the reference point closest to the myocardium can be set as an initial reference point, and the reference point can be calibrated in a clockwise or counterclockwise direction.
  • FIG. 8 is a schematic diagram of a computing unit 450, shown in accordance with some embodiments of the present application.
  • the computing unit 450 can include a displacement determining sub-unit 810, a strain determining sub-unit 820, and a stress determining sub-unit 830.
  • the individual units shown may be independent of one another or may be associated with each other.
  • the strain can be determined based on the determined displacement; the stress can be determined based on the determined strain.
  • the displacement determination sub-unit 810 can determine the displacement of the reference point.
  • the displacement is the displacement of the corresponding reference point in the same model space in different vessel models.
  • the displacement can be a relative displacement.
  • the spatial coordinate of a reference point in the vessel model of the first phase is A.
  • the spatial coordinate of the corresponding reference point is B
  • the relative displacement of the reference point from the first phase to the second phase is a space vector pointing from point A to B.
  • the displacement calculation result may be stored in the storage module 330.
  • the strain determination sub-unit 820 can determine the strain of the corresponding reference point.
  • the corresponding strain can be determined accordingly, such as according to the Green's strain tensor or the like.
  • the strain results can be stored in the storage module 330.
  • the stress determination sub-unit 830 can determine the stress experienced by the corresponding reference point.
  • the determination of the stress can be determined based on the correspondence between strain and stress. For example, the elastic modulus of the segment of the blood vessel to be studied can be found by means of a look-up table, and the stress is determined according to the elastic modulus and strain.
  • the stress results can be stored in the storage module 330.
  • the vascular stress-strain state acquisition process may include acquiring multi-temporal vascular data and establishing a blood vessel model 910, extracting a portion 920 of interest in the vascular model, and setting a plurality of reference points 930 on the extracted vascular model to match corresponding reference points 940 of different phases
  • the reference point displacement 950 is calculated from the data of the multi-phase phase and the strain and stress 960 of the blood vessel are calculated from the reference point displacement.
  • multi-temporal vessel data can be acquired and a blood vessel model established.
  • acquiring multi-temporal vessel data operations may be performed by data loading unit 410.
  • the blood vessel data may include blood vessel image data that the data loading unit 410 can receive.
  • the blood vessel data may be an angiographic image, or electronic data.
  • a description of an example of a blood vessel data type can be referred to the description in the data loading unit 410.
  • the loaded blood vessel data may be image data corresponding to a plurality of sets of blood vessel states of different phases. For example, the plurality of sets of different phases may constitute a partial or complete cardiac cycle.
  • the part of the cardiac cycle may be a time period corresponding to a diastolic process, or a time period corresponding to a cardiac contraction process.
  • the vascular state corresponding image data of the plurality of different phases may reflect the state change of the partial blood vessel within the corresponding time period.
  • the number of groups of image data of the plurality of groups may be determined by the user or determined by device performance. For example, the number of sets may be less than or equal to the upper limit of the number of image data sets that the data acquisition device 110 used for imaging may acquire or generate during a time period of one cardiac cycle.
  • the number of groups can be determined according to the doctor's judgment on the difficulty of diagnosis of the disease; the greater the difficulty of diagnosis, the more the number of groups required can be.
  • the vascular image data may be an angiographically enhanced image.
  • the image output by the data acquisition device 110 may include blood vessels and other body tissues.
  • the other body tissues may include muscle tissue, bones, body organs, and the like.
  • the blood vessels and other body tissues may cross each other.
  • an angiographic enhancement process can be performed on the output image of the data acquisition device 110 to detach portions of the blood vessel.
  • the operation of establishing a blood vessel model can also be performed by data loading unit 410.
  • the blood vessel model can be a three-dimensional blood vessel model. If multi-temporal vascular data is loaded, a corresponding multi-temporal three-dimensional vascular model can be established.
  • the method of model construction can be referred to the description in the data loading module 410.
  • the portion of interest in the vascular model can be extracted.
  • the operation This can be performed by the blood vessel extraction unit 420.
  • the portion of interest may be determined based on the symptoms of the subject. For example, if an object feels angina when the heart contracts, the doctor may determine that there may be a lesion in the coronary part of the subject's heart, and the coronary vessel may be identified as the portion of interest. For another example, if the subject may feel that some of the capillaries are densely distributed, the doctor may judge that there is a lesion in the part of the capillaries of the subject and determine the part as the part of interest.
  • the portion of interest may be input by the user through the interaction device 140.
  • the portion of interest in the extracted blood vessel model is a function that the blood vessel extraction unit 420 can perform to segment the blood vessel.
  • the extraction may be automatic extraction and/or semi-automatic extraction.
  • the method regarding blood vessel segmentation can be referred to the description in the blood vessel extraction unit 420 and the blood vessel automatic segmentation subunit 610 and the blood vessel semi-automatic segmentation subunit 620.
  • the result of the operation of the extraction operation may be the acquisition of a blood vessel or a segment of a blood vessel.
  • the operation of setting the reference point can be performed by the blood vessel blocking unit 430 and the blood vessel contouring stator unit 720 therein. In order to analyze changes in the multi-temporal vascular model, it can be selected by studying the corresponding point changes on the different phase vascular models.
  • the previously extracted vessel model may be first partitioned, which may refer to the segmentation method described in vessel block unit 430.
  • the blood vessels can be sectioned along the vertical direction of the vessel centerline to yield a series of vessel segments. When the slice is sufficiently large, the thickness of the vessel segment along the centerline direction may be disregarded.
  • the annular contour of the vessel segment can be extracted by the vessel contouring stator unit 720 and a reference point can be placed on its contour. The method of setting the reference point can be referred to the description in the vessel outline unit 720.
  • corresponding reference points for different phases can be matched.
  • the matching operation can be performed by the blood vessel matching unit 440.
  • the reference points on them need to be matched before a one-to-one comparison study can be performed.
  • a reference point in the first phase phase vessel model cannot be compared to other reference points in the second phase phase vessel model.
  • the reference point matching operation can be referred to the description in the blood vessel matching unit 440.
  • the reference point displacement can be calculated from the data of the multiphase phase.
  • the operation of the reference point displacement calculation can be performed by the displacement determination sub-unit 810.
  • the displacement of the reference point is the relative displacement of the adjacent phase.
  • the reference point in the first phase of the vessel model is A1
  • the reference point in the second phase phase vessel model is A2
  • the reference point in the third phase phase vessel model is A3.
  • the calculated reference point displacement is the displacement from A1 to A2 and from A2 to A3
  • the displacement does not calculate the displacement from A1 to A3.
  • continuous displacement change information of the reference point in the multi-phase phase vessel model is obtained.
  • the displacement calculation operation may refer to the description in the displacement determination sub-unit 810.
  • the strain and force of the blood vessel can be calculated from the reference point displacement.
  • the strain and stress may be performed by strain determining subunit 820 and stress determining subunit 830.
  • the continuous strain change and the corresponding stress change can be determined according to the continuous displacement change information in 950.
  • the method of determining the strain stress may refer to the description in the strain determining subunit 820 and the stress determining subunit 830.
  • step 920 can be omitted.
  • the operation of setting a reference point may be performed on the entire established blood vessel model. This may result in an increase in the amount of calculations, but it is also possible to continue the operation after 930.
  • FIG. 10 is an exemplary flow diagram of setting a reference point on an extracted vessel model, in accordance with some embodiments of the present application.
  • the process of setting a reference point on the extracted vessel model can include segmenting the extracted vessel model into a plurality of slices 1010, extracting a vessel slice profile 1020, and setting a number of reference points 1030 based on the vessel profile.
  • the extracted vessel model can be segmented into several slices.
  • the slicing operation can be performed by the blood vessel blocking unit 430.
  • the segmentation may be to cut a blood vessel into a number of blood vessel segments along a vertical vessel extension direction, which may be described with reference to the description in the segmentation unit 430.
  • the contour of the blood vessel slice can be extracted.
  • the operation of contour extraction may be performed by vessel contouring stator unit 720.
  • the contour of the blood vessel slice may be the contour of the inner wall of the blood vessel, the contour of the outer wall of the blood vessel or the like may describe a closed curve of the annular structure of the blood vessel slice.
  • the method of determining the contour of the blood vessel slice can be referred to the description in the vessel outline unit 720.
  • the reference point setting operation can be performed by the blood vessel contouring stator unit 720.
  • the reference The point setting operation may be to discretize the blood vessel contour curve.
  • the discretization processing method may refer to the description in the blood vessel contour unit 720.
  • the vessel under investigation is a myocardial surface vessel, and the reference point can be indexed with the point closest to the myocardium as the initial point, counterclockwise or clockwise.
  • FIG. 11 is an exemplary flow diagram of segmenting an extracted vessel model into several vessel segments, in accordance with some embodiments of the present application.
  • the process of segmenting the extracted vessel model into several vessel segments may include determining a centerline 1110 of the extracted vessel model, dividing the centerline equidistant into segments 1120, and determining that the vessel corresponding to each segment of the segment is a vessel slice.
  • each of the above operations may be performed by the blood vessel blocking unit 430.
  • the centerline of the extracted vessel model can be determined.
  • the determining the centerline operation can be performed by the centerline extraction sub-unit 710.
  • the vessel centerline may refer to an imaginary line located along the vessel's direction inside the vessel. The method of determining the center line of the blood vessel can be referred to the description in the center line extraction subunit 710.
  • the centerline can be equally divided into segments.
  • the dividing operation can be performed by centerline extraction sub-unit 710.
  • the number of equal distances divided into segments may be determined by the computing device processing power or processing result required accuracy.
  • the centerline is divided to divide the extracted vessel model into a number of units, which are separately analyzed and then integrated. If the number of units is too large, the amount of calculation may increase and the processing time may be extended, but the accuracy of the corresponding integrated processing result may be improved. Therefore, the user can manually select the number of units to be divided according to the specific diagnosis needs.
  • the doctor may wish to obtain the treatment result as soon as possible, and the corresponding number of divisions can be reduced. For example, if an object is still unable to detect the cause after multiple diagnoses, the doctor may wish to obtain a more accurate treatment result, and the corresponding number of divisions may be increased.
  • the operation of determining a blood vessel slice can be performed by a centerline extraction sub-unit 710. After dividing the center line segment of the blood vessel, the corresponding blood vessel wall range can be determined according to the center line segment. In some embodiments, one end of the centerline segment can be scanned to the other end in a plane that is always perpendicular to the centerline segment, the wall of the vessel involved in the plane being determined as the vessel wall corresponding to the segment of the segment. The determined vessel wall can be considered a blood vessel slice. Thickness of the vessel section along the vessel when the center segment is selected sufficiently If it is not considered, the blood vessel slice is a ring structure considering the cross-sectional shape of the blood vessel.
  • the data loading unit 410 constructs a three-dimensional blood vessel model based on the image data of the data acquisition device, and the model includes the blood vessel 1 and the blood vessel 2.
  • blood vessel 1 can be a cardiac coronary artery and blood vessel 2 can be a cardiac vein. Studying and analyzing blood vessel 1 requires separation of blood vessel 1 and blood vessel 2.
  • the blood vessel extraction unit 420 divides the blood vessel 1 and the blood vessel 2 and extracts the blood vessel 1. The segmentation may be to automatically segment the coronary artery from the vein, or the user manually identifies the vessel 1 in the vessel model in A and extracts it.
  • the blood vessel 1 can be divided into a plurality of blood vessel segments.
  • the segment of the blood vessel in the dashed box a may be one of several segments of the vessel after the segmentation. If the width of the broken line frame ⁇ is sufficiently small, the thickness of the blood vessel segment in the direction in which the blood vessel extends is not considered, and the blood vessel segment in the broken line frame ⁇ is a blood vessel slice.
  • the cross section of the blood vessel in the dashed box in B is a closed annular structure.
  • the contour of the blood vessel slice can be extracted by the blood vessel contouring unit 720, and the extraction method of the contour can be referred to the description in the blood vessel contouring unit 720.
  • the extracted contour is a circular curve, which can be discretized for analysis.
  • the curves are equally spaced into several reference points.
  • the number of reference points may be any integer from 50 to 5000, such as 100 or 200.
  • the number of reference points may be determined according to the distance between the reference points. For example, when setting the reference point, it can be determined that the distance between the two reference points is a fixed value, and the number of reference points is determined according to the selection of the fixed value. After determining the reference point, the reference point needs to be calibrated.
  • the lowest point is set as the starting reference point 1, and the reference point is calibrated in the counterclockwise direction until the last reference point is calibrated.
  • the determination of the reference point 1 may be the point at which the blood vessel slice is closest to the surface of the human organ. In some embodiments, the determination of the reference point 1 may be the point closest to the origin of the spatial coordinate system or a certain axis of the vessel model.
  • T1 and T2 are the same blood vessel slice maps of two different periods, and both blood vessel sections are subjected to contour determination and reference point setting.
  • the blood vessel slice has been expanded in the lateral width for some reason from the time of T1 to T2, and the corresponding reference point has occurred to some extent.
  • the coordinates of N1 and N2 can be placed in a three-dimensional coordinate system M (the z-axis is omitted), and the displacements of the reference points N1 to N2 can be obtained. This displacement can be represented by a vector pointing from N1 to N2.
  • the displacement of all reference points on the blood vessel slice from the T1 phase to the T2 phase can be determined.
  • Each blood vessel slice can obtain the displacement of all reference points on the extracted blood vessels from T1 to T2 phase by the sequential method, and then the extracted blood vessels can be determined from the T1 phase to the T2 phase according to the relationship between displacement and strain and stress. Strain and stress.
  • the T2 to T3 phase, T3 to T4 phase, and a complete cardiac cycle are sequentially calculated, and the strain stress changes of various parts of the blood vessel during the time period can be determined.
  • the output evaluating the vascular state result can include acquiring stress or strain data 1410, comparing 1420 with reference vascular stress or strain data, evaluating vascular state 1430 based on the comparison, and output evaluating vascular status result 1440.
  • the above steps may be performed by analysis module 340.
  • stress or strain data can be obtained.
  • the stress or strain data may be stress or strain data acquired in 960.
  • the stress or strain experienced by a reference point on a certain vessel model at different phases may be different.
  • the data reflecting the stress or strain characteristics at the reference point may be data of the maximum stress or strain experienced by the point over a period of time, or one or more cardiac cycles, or over a period of time or one or more cardiac cycle stresses. Or the cumulative average of the strain data.
  • the blood vessel may have a maximum stress that can withstand, and the maximum stress or strain value that the point can withstand in different phases can be used as data reflecting the stress or strain characteristics at that point.
  • a reference point on a blood vessel is subjected to a degree of stress or strain for a prolonged period of time, and then there may be a risk of rupture at the reference point due to accumulation of time.
  • the cumulative average of the corresponding force or strain data of the point at different points can be used as the stress or strain characteristic data of the point.
  • the comparison operation can be performed by the calculation result comparison unit 510.
  • the comparison may be comparing the reference point stress-strain characteristic data in 1410 with reference vascular stress or strain data.
  • the reference blood vessel stress or strain data may be data stored in the storage module 330, data acquired through the network 180, or data input by the user.
  • the reference vascular stress or strain data can be The blood vessel state reference result described in unit 510 is calculated.
  • the blood vessel status can be evaluated based on the comparison result.
  • the comparison result of the vascular stress-strain characteristic data and the reference data may correspond to a comparative conclusion, such as normal, early warning, danger, extreme danger, and the like.
  • the reference data may be some data intervals corresponding to respective comparison conclusions. When the blood vessel stress or strain characteristic data falls within a certain interval, the corresponding comparison conclusion is corresponding.
  • the evaluation of the blood vessel state conclusion can be processed by the result generation unit 520 to generate a statistical chart, a statistical table, a document in a fixed format, and an audio file.
  • the related generation manner can refer to the description in the result generation unit 520.
  • an assessment of the blood vessel status result can be output.
  • the operation of the output may be performed by result transfer sub-unit 530.
  • the output manner of the result can be referred to the description in the result transmission subunit 530.
  • the present application uses specific words to describe embodiments of the present application.
  • a "one embodiment,” “an embodiment,” and/or “some embodiments” means a feature, structure, or feature associated with at least one embodiment of the present application. Therefore, it should be emphasized and noted that “an embodiment” or “an embodiment” or “an alternative embodiment” that is referred to in this specification two or more times in different positions does not necessarily refer to the same embodiment. . Furthermore, some of the features, structures, or characteristics of one or more embodiments of the present application can be combined as appropriate.
  • aspects of the present application can be illustrated and described by a number of patentable categories or conditions, including any new and useful process, machine, product, or combination of materials, or Any new and useful improvements. Accordingly, various aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.) or by a combination of hardware and software.
  • the above hardware or software may be referred to as a "data block,” “module,” “engine,” “unit,” “component,” or “system.”
  • aspects of the present application may be embodied in a computer product located in one or more computer readable medium(s) including a computer readable program code.
  • the computer readable signal medium may contain a propagated data message containing a computer program code The number, for example on the baseband or as part of the carrier.
  • the propagated signal may have a variety of manifestations, including electromagnetic forms, optical forms, and the like, or a suitable combination.
  • the computer readable signal medium may be any computer readable medium other than a computer readable storage medium that can be communicated, propagated, or transmitted for use by connection to an instruction execution system, apparatus, or device.
  • Program code located on a computer readable signal medium can be propagated through any suitable medium, including a radio, cable, fiber optic cable, RF, or similar medium, or a combination of any of the above.
  • the computer program code required for the operation of various parts of the application can be written in any one or more programming languages, including object oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python. Etc., regular programming languages such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages.
  • the program code can run entirely on the user's computer, or run as a stand-alone software package on the user's computer, or partially on the user's computer, partly on a remote computer, or entirely on a remote computer or server.
  • the remote computer can be connected to the user's computer via any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (eg via the Internet), or in a cloud computing environment, or as a service.
  • LAN local area network
  • WAN wide area network
  • an external computer eg via the Internet
  • SaaS software as a service
  • numbers describing the number of components and attributes are used, it should be understood that such The numbers used in the examples are modified in some examples using the modifiers "about”, “approximately” or “substantially”. Unless otherwise stated, “about”, “approximately” or “substantially” indicates that the number is allowed to vary by ⁇ 20%. Accordingly, in some embodiments, numerical parameters used in the specification and claims are approximations that may vary depending upon the desired characteristics of the particular embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method of general digit retention. Although numerical fields and parameters used to confirm the breadth of its range in some embodiments of the present application are approximations, in certain embodiments, the setting of such values is as accurate as possible within the feasible range.

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Abstract

一种获取血管壁应力应变状态的方法。所述获取血管应力应变状态的方法可以包括：获取对应于一个血管的第一时相血管数据，获取对应于所述血管的第二时相血管数据，基于所述第一时相血管数据建立第一时相的血管模型，基于所述第二时相血管数据建立第二时相的血管模型，提取所述第一时相血管模型中感兴趣的部分，提取所述第二血管模型中所述感兴趣的部分，在所述第一时相血管模型中感兴趣的部分设置一个参考点，在所述第二时相血管模型中所述感兴趣的部分找到所述参考点，确定所述参考点的位移以及根据所述参考点的位移确定所述参考点处的应力或应变。

Description

血管壁应力应变状态获取方法及系统 技术领域
本申请涉及血管壁状态获取方法及系统，尤其是涉及基于多时相图像数据重建血管模型并计算血管壁应力应变的方法和系统。
背景技术
成像在医疗领域具有重要的作用。成像技术种类繁多，可以包括，例如，数字减影心血管造影术(Digital Subtraction Angiography(DSA))、磁共振成像(Magnetic Resonance Imaging(MRI))、磁共振血管造影术(Magnetic Resonance Angiography(MRA))、计算机断层扫描(Computed tomography(CT))、CT血管造影(Computed Tomography Angiography(CTA))、超声扫描(Ultrasound Scanning(US))、正电子发射断层扫描(Positron Emission Tomography(PET))、单光子发射计算机断层成像(Single-Photon Emission Computerized Tomography(SPECT))、SPECT-MR、CT-PET、CE-SPECT、DSA-MR、PET-MR、PET-US、SPECT-US、经颅磁刺激磁共振成像(TMS(transcranial magnetic stimulation)-MR)、US-CT、US-MR、X射线-CT、X射线-PET、X射线-US等，或者上述成像技术的任意组合。根据以上成像技术，可以在计算机上建立人体组织器官的三维模型，包括对血管模型的建立。人体的血管在一个心动周期内由于血液流速的不同，对血管壁的压力也不同。血管壁也会在心动周期内产生不同的内部应力变化。研究血管壁上的应力状态可以有效的帮助医生来判断血管的哪一部分有潜在的破裂风险。利用多时相的血管成像数据可以建立完整心动周期内的血管三维模型。利用该模型来获取血管壁的应力状态有助于精确的诊断疾病和辅助治疗。
简述
本申请的一个方面是关于一种获取血管壁应力应变状态的方法。所述获取血管应力应变状态的方法可以包括：获取对应于一个血管的第一时相血管数据，获取对应于所述血管的第二时相血管数据，基于所述第一时相血管数据建立第一时相的血管模型，基于所述第二时相血管数据建立第二时相的血管模型，提取所 述第一时相血管模型中感兴趣的部分，提取所述第二血管模型中所述感兴趣的部分，在所述第一时相血管模型中感兴趣的部分设置一个参考点，在所述第二时相血管模型中所述感兴趣的部分找到所述参考点，确定所述参考点的位移以及根据所述参考点的位移确定所述参考点处的应力或应变。
本申请的另一个方面是关于一种非暂时性的计算机可读介质。所述非暂时性的计算机可读介质可以包括可执行指令。所述指令被至少一个处理器执行时，可以导致所述至少一个处理器实现所述获取血管壁应力应变状态的方法。
本申请的另一个方面是关于一种提获取血管壁应力应变状态的系统。所述获取血管壁应力应变状态的系统可以包括：至少一个处理器，和所述可执行指令。
本申请的另一个方面是关于一种系统。所述系统可以包括：至少一个处理器以及用来存储指令的存储器。所述指令被所述至少一个处理器执行时，可以导致所述系统实现的操作包括所述获取血管壁应力应变状态的方法。
根据本申请的一些实施例，所述获取血管壁应力应变状态的方法可以进一步包括：将所述应力或应变与参考数据进行比较，根据比较结果评估血管状态以及将血管状态评估结果传输给用户。
根据本申请的一些实施例，所述血管状态评估结果中的结果呈现形式可以为下列形式中的至少一种，包括图、表、固定格式的文字以及音频等。
根据本申请的一些实施例，所述参考数据可以存储在一个存储设备中。
根据本申请的一些实施例，所述将血管状态评估结果传输给用户可以包括将血管状态评估结果传输到至少一个用户的用户终端。
根据本申请的一些实施例，所述将所述确定的应力或应变与参考数据进行比较可以包括：确定所述参考点应力或应变的特征值以及将所述特征值与所述参考数据进行比较。
根据本申请的一些实施例，所述参考点的所述应力或应变的特征值可以包括所述参考点在不同时相中的最大应力或应变值。
根据本申请的一些实施例，所述参考点的所述应力或应变的特征值可以包括所述参考点在不同时相中的应力或应变的平均值
根据本申请的一些实施例，所述在所述第一时相血管模型中感兴趣的部 分设置一个参考点可以包括：将所述血管分段为若干血管切片，提取所述若干血管切片中一个血管切片的轮廓以及基于所述血管切片的轮廓设置所述参考点。
根据本申请的一些实施例，所述将血管分段为若干血管切片可以包括：确定所述血管的中心线，将所述中心线划分为若干中心线段以及确定所述若干中心线段中的一段中心线段对应的血管段为所述若干血管切片中的一个血管切片。
根据本申请的一些实施例，所述基于血管切片的轮廓设置所述参考点可以包括：在所述血管轮廓上均匀等间距设置一定数量的参考点以及从所述一定数量的参考点中选取所述参考点。
根据本申请的一些实施例，所述获取血管壁应力应变状态的方法可以进一步包括在所述设置的参考点中选定一个初始点，以及从初始点开始以逆时针或顺时针的方向对所述一定数量的参考点依次编号
根据本申请的一些实施例，所述第一时相的血管模型可以包括心脏血管模型，所述心脏血管模型可以包括冠脉和静脉，所述提取所述第一时相血管模型中感兴趣的部分可以包括自动提取所述心脏血管模型中的冠脉。
根据本申请的一些实施例，所述提取所述第一时相血管模型中感兴趣的部分可以包括：从用户接收所述血管的两个端点的信息以及根据所述接收到的血管的两个端点的信息，从所述血管中提取所述两个端点之间的血管段。
根据本申请的一些实施例，所述确定所述参考点的位移可以包括确定相邻时相间的所述参考点的位移。
本申请的一部分附加特性可以在下面的描述中进行说明。通过对以下描述和相应附图的检查或者对实施例的生产或操作的了解，本申请的一部分附加特性对于本领域技术人员是明显的。本披露的特性可以通过对以下描述的具体实施例的各种方面的方法、手段和组合的实践或使用得以实现和达到。
附图描述
在此所述的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的限定。在各图中，相同标号表示相同部件。
图1A和图1B是根据本申请的一些实施例所示的包括血管状态分析系 统；
图2A是根据本申请的一些实施例所示的一个计算设备的结构，该计算设备可以实施本申请中披露的特定系统；
图2B是根据本申请的一些实施例所示的一个移动设备的结构示意图，该移动设备可以实施本申请中披露的特定系统
图3是根据本申请的一些实施例所示的处理设备的示例性模块示意图；
图4是根据本申请的一些实施例所示的一个处理模块的示意图；
图5是根据本申请的一些实施例所示的一个分析模块的示意图；
图6是根据本申请的一些实施例所示的一个血管提取单元的示意图；
图7是根据本申请的一些实施例所示的一个血管分块单元的示意图；
图8是根据本申请的一些实施例所示的一个计算单元的示意图；
图9是根据本申请的一些实施例所示的一个获取血管应力应变状态的示例性流程图；
图10是根据本申请的一些实施例所示的一个在提取的血管模型上设置参考点的示例性流程图；
图11是根据本申请的一些实施例所示的一个将提取的血管模型分段为若干血管切片的示例性流程图；
图12是根据本申请的一些实施例所示的一个在血管模型上设置参考点的示意图；
图13是根据本申请的一些实施例所示的一个根据不同期相血管模型确定参考点位移的示意图；以及
图14是根据本申请的一些实施例所示的一个输出评估血管状态结果的示例性流程图。
具体描述
为了更清楚地说明本申请的实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单的介绍。显而易见地，下面描述中的附图仅仅是本申请的一些示例或实施例，对于本领域的普通技术人员来讲，在不付出创造 性劳动的前提下，还可以根据这些附图将本申请应用于其他类似情景。除非从语言环境中显而易见或另做说明，图中相同标号代表相同结构或操作。
如本申请和权利要求书中所示，除非上下文明确提示例外情形，“一”、“一个”、“一种”和/或“该”等词并非特指单数，也可包括复数。一般说来，术语“包括”与“包含”仅提示包括已明确标识的步骤和元素，而这些步骤和元素不构成一个排它性的罗列，方法或者设备也可能包含其他的步骤或元素。
虽然本申请对根据本申请的实施例的数据处理系统中的某些模块做出了各种引用，然而，任何数量的不同模块可以被使用并运行在一个通过网络与该系统连接的客户端和/或服务器上。所述模块仅是说明性的，并且所述系统和方法的不同方面可以使用不同模块。
本申请中使用了流程图用来说明根据本申请的实施例的数据处理系统所执行的操作步骤。应当理解的是，显示在前面或后面的操作步骤不一定按照顺序来精确地执行。相反，可以按照倒序或同时处理各种步骤。同时，也可以将其他操作步骤添加到这些过程中，或从这些过程移除某一步或数步操作。
在图像数据处理过程中，“图像分割”、“图像提取”、“图像分类”可以相互转化，均表达从大范围区域内选取符合某条件的图像。在一些实施例中，成像系统可以包括一种或多种形态。所述形态可以包括数字减影血管造影(DSA)、磁共振成像(MRI)、磁共振血管造影(MRA)、计算机断层扫描(CT)、计算机断层扫描血管造影(CTA)、超声波扫描(US)、正电子发射断层扫描术(PET)、单光子发射计算机断层扫描(SPECT)、SPECT-MR、CT-PET、CE-SPECT、DSA-MR、PET-MR、PET-US、SPECT-US、TMS-MR、US-CT、US-MR、X射线-CT、X射线-PET、X射线-US、视频-CT、视频-US和/或类似的一种或多种的组合。在一些实施例中，成像扫描的目标可以是器官、机体、物体、损伤部位、肿瘤等一种或多种的组合。在一些实施例中，成像扫描的目标可以是头部、胸腔、腹部、器官、骨骼、血管等一种或多种的组合。在一些实施例中，扫描的目标可以为一个或多个部位的血管组织。在一些实施例中，图像可以是二维图像和/或三维图像。在二维图像中，最细微的 可分辨元素可以为像素点(pixel)。在三维图像中，最细微的可分辨元素可以为体素点(voxel)。在三维图像中，图像可由一系列的二维切片或二维图层构成。
图像分割过程可以基于图像的像素点(或体素点)的相应特征进行。在一些实施例中，所述像素点(或体素点)的相应特征可以包括纹理结构、灰度、平均灰度、信号强度、颜色饱和度、对比度、亮度等一种或多种的组合。在一些实施例中，所述像素点(或体素点)的空间位置特征也可以用于图像分割过程。
需要注意的是，以上对于图像数据处理系统的描述，仅为描述方便，并不能把本申请限制在所举实施例范围之内。可以理解，对于本领域的技术人员来说，在了解该系统的原理后，可能在不背离这一原理的情况下，对各个模块进行任意组合，或者构成子系统与其他模块连接，对实施上述方法和系统的应用领域形式和细节上的各种修正和改变。
图1A是根据本申请的一些实施例所示的包括血管状态分析系统100。该血管状态分析系统100可以包括数据采集设备110、处理设备120、存储设备130和交互设备140。数据采集设备110、处理设备120、存储设备130和交互设备140相互之间可以通过网络180进行通信。
数据采集设备110可以是一个采集数据的设备。所述数据可以包括图像数据、对象特征数据等。在一些实施例中，所述数据采集设备110可以包括一个成像设备。所述成像设备可以采集所述图像数据。所述成像设备可以是磁共振成像仪(magnetic resonance imaging,MRI)、电子计算机断层扫描仪(computed tomography,CT)、正电子发射型计算机断层显像仪(positron emission computed tomography,PET)、B超仪(b-scan ultrasonography)、超声诊断仪(diasonography)、热断层扫描仪(thermal texture maps,TTM)、医用电子内窥镜(medical electronic endoscope,MEE)等中的一种或多种的组合。所述图像数据可以是包括对象的血管、组织或器官的图片或数据。在一些实施例中，所述数据采集设备可以包括一个对象特征采集设备。所述对象特征采集设备可以采集对象的心率、心律、血压、血流速率、血液粘稠度、心输出量、心肌质量、血管流阻，以及/或其他与血管、组织或器官相关的对象特 征数据。在一些实施例中，所述对象特征采集设备可以获取对象年龄、身高、体重、性别等其他对象特征数据。在一些实施例中，所述图像数据和对象特征数据可以是多时相数据。例如，所述多时相数据可以是在不同的时间点或时相获得的对象身上相同或近似位置的数据。在一些实施例中，所述对象特征采集设备可以集成在所述成像设备中，从而同时采集图像数据和对象特征数据。在一些实施例中，所述数据采集设备110可以通过网络180将其所采集的数据发送至处理设备120、存储设备130和/或交互设备140等。
处理设备120可以对数据进行处理。所述数据可以是通过数据采集设备110采集到的数据，从存储设备130中读取的数据，从交互设备140中获得的反馈数据，如用户的输入数据，或通过网络180从云端或者外接设备中获得的数据等。在一些实施例中，所述数据可以包括图像数据、对象特征数据、用户输入数据等。所述处理可以包括在图像数据中选择感兴趣的区域。所述感兴趣的区域可以由处理设备120自行选择或根据用户输入数据选择。在一些实施例中，选择的感兴趣区域可以是血管、组织或者器官等。例如，所述感兴趣区域可以是动脉血管，如冠状动脉、腹部动脉、大脑动脉、下肢动脉等。处理设备120可以进一步对所述图像中对感兴趣的区域进行分割。图像分割的方法可以包括基于边缘的图像分割方法，如Perwitt算子法、Sobel算子法、梯度算子法、Kirch算子法等，基于区域的图像分割方法，如区域生长法、阈值法、聚类法等以及其他分割方法，如基于模糊集、神经网络的方法等。
处理设备120可以对所述感兴趣区域进行模型重建。模型的选择可以基于对象特征数据、感兴趣区域的特征等。例如，如果选定了感兴趣的区域为冠状动脉，处理设备120可以对包含冠状动脉的图像进行分割从而提取出冠状动脉的图像。然后，处理设备120可以根据对象特征、冠状动脉一般特征、冠状动脉图像特征等进行模型的重建。重建的模型可以与冠状动脉血管的形状相对应，或与冠状动脉中血液流动的形态相对应。在建立感兴趣区域的模型后，处理设备120可以根据模型进行分析。
在一些实施例中，处理设备120可以获得多时相的数据，如对象在5个不同时间点上冠状动脉区域的图像。在这种情况下，处理设备120可以对 不同时相的感兴趣区域(例如，整个冠状动脉，冠状动脉上的分支，或者冠状动脉的血液入口截面等)的图像分别构建模型，再对模型依次进行分析和计算。在一些实施例中，处理设备120可以对所述不同时相的模型进行网格化处理，并对网格化处理后的模型进行相互关联，从而降低计算量、提高计算准确度。关于网格化处理和模型相互关联的说明可以参见本申请其他地方的描述，例如，图6，图11及其描述。在一些实施例中，所述分析和计算的结果可以包括血管、组织、或器官的物理状态和相关系数或参数。例如，对冠状动脉模型进行分析和计算的结果可以包括冠状动脉的血流动力学参数，如血流速率、血液压力、血管壁应力、血管壁切应力、血流储备份数(Fractional Flow Reserve,FFR)等中的一种或多种的组合。在一些实施例中，处理设备120可以根据不同时相的分析和计算结果生成所述物理状态和/或相关系数或参数与时相或时间的关系(例如，血液动力学参数随时间的变化)。该关系可以用曲线或者对照表的方式体现。基于所述曲线或对照表，处理设备120可以获得任意时相的感兴趣区域的物理状态和/或相关系数或参数。
在一些实施例中，处理设备120可以对其获得的数据或处理结果进行降噪或平滑处理。在一些实施例中，处理设备120可以将其获得的数据或处理结果发送至存储设备130进行存储，或者发送至交互设备140进行显示。所述处理结果可以是处理过程中产生的中间结果，如感兴趣区域的模型，也可以是处理的最终结果，如分析和计算得出的血流动力学参数等。在一些实施例中，处理设备120可以是一个或多个处理元件或设备，如中央处理器(central processing unit，CPU)、图形处理器(graphics processing unit，GPU)、数字信号处理器(digital signal processor，DSP)、系统芯片(system on a chip，SoC)、微控制器(microcontroller unit，MCU)等。在一些实施例中，处理设备120也可以是特殊设计的具备特殊功能的处理元件或设备。处理设备120可以是本地的，或相对于数据采集设备110是远程的。
存储设备130可以储存数据或信息。所述数据或信息可以包括数据采集设备110获取的数据、处理设备120产生的处理结果或控制指令、以及交互设备140所接收到的用户输入数据等。存储设备130可以是一种或多种可以读取或写入的存储媒介，包括静态随机存储器(static random access memory, SRAM)，随机存储器(random-access memory,RAM)、只读存储器(read-only memory,ROM)、硬盘、闪存等。在一些实施例中，存储设备130也可以是远程的存储器，如云盘等。
交互设备140可以接收、发送，以及/或显示数据或信息。所述接收的数据或信息可以包括数据采集设备110获取的数据、处理设备120产生的处理结果、存储设备130存储的数据等。例如，交互设备140显示的数据或信息可以包括数据采集设备110获得的心血管的实际图像150、处理设备120根据实际图像150所建立的心血管模型160，以及处理设备120从心血管模型160中提取出的冠状动脉模型170等。显示的形式可以包括二维或三维的医学图像、几何模型及其网格分析、矢量图(如速度矢量线)、等值线图、填充型的等值线图(云图)、XY散点图、粒子轨迹图、模拟流动效果等一种或多种组合。又例如，交互设备140发送的数据或信息可以包括用户的输入信息。交互设备140可以接收用户输入的处理设备120的一个或多个运行参数，并发送到处理设备120。
在一些实施例中，交互设备140可以包括一个用户交互界面。用户可以通过特定的交互装置，如鼠标、键盘、触摸板、麦克风等向交互设备140输入一个用户输入数据。例如，用户可以点击交互设备140所显示的模型并选择模型中感兴趣的区域。又例如，用户可以选择交互设备140所显示的血管模型中任意的位置，交互设备140可以从处理设备120获取并显示该位置的血流速、血压、血流量等。
在一些实施例中，交互设备140可以是显示屏等具有显示功能的设备。在一些实施例中，交互设备140可以具有处理设备120部分或全部的功能。例如，交互设备140可以对处理设备120生成的结果进行平滑、降噪、变色等操作。举例说明，变色操作可以将一个灰度图变成彩图，或将一个彩图变成一个灰度图。在一些实施例中，交互设备140与处理设备120可以是一个集成的设备。所述集成的设备可以同时实现处理设备120和交互设备140的功能。在一些实施例中，交互设备140可以包括台式电脑、服务器、移动设备等。移动设备可以包括笔记本电脑、平板电脑、ipad、交通工具(例如，机动车、船、飞机等)的内置设备、可穿戴设备等。在一些实施例中，交互 设备140可以包括或连接到显示装置、打印机、传真等。
网络180可以用于血管状态分析系统100内部的通信，接收系统外部的信息，向系统外部发送信息等。在一些实施例中，数据采集设备110、处理设备120和交互设备140之间可以通过有线连接、无线连接、或其结合的方式接入网络180。网络180可以是单一网络，也可以是多种网络的组合。在一些实施例中，网络180可以包括但不限于局域网、广域网、公用网络、专用网络、无线局域网、虚拟网络、都市城域网、公用开关电话网络等中的一种或几种的组合。在一些实施例中，网络180可以包括多种网络接入点，例如有线或无线接入点、基站或网络交换点，通过以上接入点使数据源连接网络180并通过网络发送信息。
图1B所示的是一个血管状态分析系统100的另一个示意图。图1B与图1A类似。图1B中，处理设备120可以与数据采集设备110直接相连，而数据采集设备110不与网络180直接相连。
以上的描述仅仅是本发明的具体实施例，不应被视为是唯一的实施例。显然，对于本领域的专业人员来说，在了解本发明内容和原理后，都可能在不背离本发明原理、结构的情况下，进行形式和细节上的各种修正和改变。例如，数据采集设备110、处理设备120、交互设备140之间可以不通过网络180而直接进行数据或信息的交换。又例如，这些设备也可以通过可移动存储器或其他中间媒介的方式进行数据或信息的交换。
图2A是根据本申请的一些实施例所示的一个计算设备200的结构。该计算设备200可以实施本申请中披露的特定系统。本实施例中的特定系统利用功能框图解释了一个包含用户界面的硬件平台。计算设备200可以实施当前描述血管状态分析系统100中的一个或多个组件、模块、单元、子单元(例如，处理设备120，交互设备140等)。另外，血管状态分析系统100中的一个或多个组件、模块、单元、子单元(例如，处理设备120，交互设备140等)能够被计算设备200通过其硬件设备、软件程序、固件以及它们的组合所实现。这种计算机可以是一个通用目的的计算机，也可以是一个有特定目的的计算机。两种计算机都可以被用于实现本实施例中的特定系统。为了方便起见，图2A中只绘制了一台计算设备，但是本实施例所描述的进行 信息处理并推送信息的相关计算机功能是可以以分布的方式、由一组相似的平台所实施的，分散系统的处理负荷。
如图2A所示，计算设备200可以包括内部通信总线210，处理器(processor)220，只读存储器(ROM)230，随机存取存储器(RAM)240，通信端口250，输入/输出组件260，硬盘270，用户界面280。内部通信总线210可以实现计算设备200组件间的数据通信。处理器220可以执行程序指令完成在此披露书中所描述的血管状态分析系统100的一个或多个功能、组件、模块、单元、子单元。处理器220由一个或多个处理器组成。通信端口250可以配置实现计算设备200与血管状态分析系统100其他部件(比如数据采集设备110)之间数据通信(比如通过网络180)。计算设备200还可以包括不同形式的程序储存单元以及数据储存单元，例如硬盘270，只读存储器(ROM)230，随机存取存储器(RAM)240，能够用于计算机处理和/或通信使用的各种数据文件，以及处理器220所执行的可能的程序指令。输入/输出组件260支持计算设备200与其他组件(如用户界面280)，和/或与血管状态分析系统100其他组件(如数据库140)之间的输入/输出数据流。计算设备200也可以通过通信端口250从网络180发送和接收信息及数据。
图2B描述了一种移动设备的结构，该移动设备能够用于实现实施本申请中披露的特定系统。在本例中，用于显示和交互位置相关信息的用户设备是一个移动设备300。移动设备300可以包括智能手机、平板电脑、音乐播放器、便携游戏机、全球定位系统(GPS)接收器、可穿戴计算设备(如眼镜、手表等)，或者其他形式。本例中的移动设备300包括一个或多个中央处理器(CPUs)204，一个或多个图形处理器(graphical processing units(GPUs))203，一个显示202，一个内存206，一个天线201，例如一个无线通信单元，存储单元209，以及一个或多个输入/输出(input output(I/O))设备205。任何其他合适的组件，包括但不限于系统总线或控制器(图上未显示)，也可能被包括在移动设备300中。如图2B所示，一个移动操作系统207，如iOS、Android、Windows Phone等，以及一个或多个应用208可以从存储单元209加载进内存206中，并被中央处理器204所执行。应用208可能包括一个浏览器或其他适合在移动设备300上接收并处理图像或血管状态分析相关信息 的移动应用。用户与血管状态分析系统100中一个或多个组件关于图像或血管状态分析相关信息的交互可以通过输入/输出系统设备205获得并提供给处理设备120，以及/或血管状态分析系统100中的其他组件，例如：通过网络180。
图3是根据本申请的一些实施例所示的处理设备的示例性模块示意图。处理设备120可以包括接收模块310、处理模块320、存储模块330和分析模块340和输出模块350。
接收模块310可以从数据采集设备310和/或存储设备330获取图像数据、对象特征数据等。所述图像数据可以是包括对象的血管、组织或器官的图片或数据。所述对象特征数据可以包括对象的心率、心律、血压、血流速率、血液粘稠度、心输出量、心肌质量、血管流阻以及其他与血管、组织或器官相关的对象特征数据以及对象年龄、身高、体重、性别等其他对象特征数据。在一些实施例中，所述图像数据和对象特征数据可以是多时相数据。例如，所述多时相数据可以是在不同的时间点或时相获得的对象身上相同或近似位置的数据。
处理模块320可以进行数据的相关处理，以及根据相应数据构建相应图像。所述数据可以来自接收模块310、存储模块330和/或其他未示出模块。所述数据可以是通过网络180获取的外部数据资源。处理模块320处理的数据可以是关于目标物体的特殊部分的数据，例如，大脑、心脏、血管、肺、支气管等一种或多种的组合。在一些实施例中，处理模块320可以处理与血管相关的数据。处理模块320可以基于多种模式对数据进行处理，包括可选择数据参数的模式、自动处理模式、程序处理模式、函数处理模式等一种或多种的组合。在一些实施例中，用户可选择需要处理的数据，例如，可选择对图像中特定部位的血管进行处理。在一些实施例中，函数处理模式可以是基于直方图拟合的血管图像数据预处理，基于函数变换的图像数据处理，基于权重计算的图像数据处理等一种或多种的组合。所述图像数据的处理可以包括图像预处理，图像粗分割，图像特征点的追踪，和/或图像变换等。程序处理模式中，可选择不同的程序处理方法在不同阶段，针对不同需求进行数据处理。函数处理模式可以包括各类型函数处理方法，包括水平集函数法、 梯度下降法、指数函数变换法、直方图数据膨胀函数拟合等一种或多种的组合。
存储模块330可以存储数据或信息。存储的数据或信息可以是各种形式，例如，数值、信号、图像、目标物体的相关信息、命令、算法、程序等一种或多种的组合。在一些实施例中，存储数据可以是血管图像、血管图像参数、血管图像处理后的数据、或血管图像处理所应用的程序和/或算法等。
分析模块340可以进行数据分析。在一些实施例中，分析模块可以对处理模块320的计算结果进行分析。例如，将分析模块的计算结果与参考值进行比对。在一些实施例中，所述分析模块340还可以将分析的结果进行格式化的表示。例如，将分析结果绘制成图表或报告的形式。
输出模块350可以将生成的分析结果或计算数据进行输出。例如，输出模块350可以将分析结果或计算数据发送至存储设备130进行存储，或者发送至交互设备140进行显示或以其他方式(如声音等)呈现给客户。所述计算结果可以是生成的中间结果，如感兴趣区域的模型，或生成的最终结果，如分析和计算得出的血管壁应力应变参数或计算结果与时相的关系曲线或对照表等。
图4是根据本申请的一些实施例所示的一个处理模块240的示意图。处理模块可以包含一个数据载入单元410、一个血管提取单元420、一个血管分块单元430、一个血管匹配单元440和一个计算单元450。所示模块之间可以彼此直接(和/或间接)连接。
数据载入单元410可以载入血管数据以建立血管模型。所述血管数据可以是来自于接收模块310和/或存储模块330。在一些实施例中，所述血管数据可以来自于接收模块310。所述接收模块310的数据来自于数据采集设备110。例如数据采集设备110产生了一系列CT断层扫描图片。该一系列CT断层扫描图片可以经由接收模块310传输至数据载入单元410。在一些实施例中，所述血管数据可以来自于存储模块330。例如，某位对象的病例资料存储在存储模块330中，在需要调取该对象的相关诊断时的造影图片时，可以直接从存储模块330中读取数据并传输至数据载入单元410中。所述数据载入单元410可以将所获取的血管影像资料载入至数据处理模块320。
所述的血管影像资料可以是一系列的血管造影图片，或一些反应血管构造的电子数据。在一些实施例中，一系列的血管造影图片可以是某根血管或某一部分血管不同截面的影像。所述一系列血管造影图片的个数可以由数据采集设备110的处理能力或存储模块330中的对于该部分血管的信息存储量来决定。例如，某数据采集设备110的成像能力可以某段血管的x个截面，则获取的一系列血管造影图片的个数可以为x或少于x。又例如，某对象存储在存储模块330的病例资料中包含y个某段血管的截面影像，则获取的一系列血管造影图片可以为y个或小于y个。在一些实施例中，所述的血管影像资料可以是一系列的电子数据。例如，某数据采集设备110的数据输出结果形式为一模拟血管模型，所述血管模型由若干体素构成，每个体素可以有一个对应的坐标。所述每个体素的坐标的总和可以用来重建一个完整的血管模型。所述体素的坐标信息可以作为电子数据进行传输，并由数据载入单元410载入。所述电子数据的数据量大小可以由所述体素的分辨率来决定。例如，若将血管模型精细化分为更多的体素则可能产生更大的数据量。
所述的载入的血管影像资料可以是某一时相的血管状态对应的影像资料，若干组不同时相的血管状态对应的影像资料，或某段时间段内的血管状态动态影像资料。在一些实施例中，所载入的血管影像资料为某一时相的血管状态对应影像资料。例如，在一个心动周期内，心脏的收缩和舒张可能导致血管状态的变化。所述某一时相可以为心脏收缩到最小的时相，心脏舒张到最大的时相，或心脏收缩或舒张过程中的某一时相。在一些实施例中，所述载入的血管影像资料可以是若干组不同时相的血管状态对应的影像资料。例如，所述若干组不同时相可以构成一部分或完整的心动周期。所述一部分心动周期可以是心脏舒张过程所对应的时间段，或心脏收缩过程所对应的时间段。所述若干组不同时相的血管状态对应影像资料可以反映该部分血管在对应时段内的状态变化。所述若干组不同时相的组数可以由数据采集设备110的能力决定，由载入单元的处理能力决定，或由用户决定。例如，某数据采集设备110的成像能力在时间域的分辨率较高，则在所研究的时间段内可以生成较多组的血管影像。又例如，某数据载入单元410的数据处理能力有限，则载入的若干组血管影像资料组数会由数据载入单元410的处理能力所限制。又例如，对于某血管状态研究的精密度要求很高，则需要较 多组的血管影像资料才足够还原出在所研究时间段内血管状态的变化。在一些实施例中，所述血管影像资料也可以是某段时间内的血管动态影像资料。例如，某数据采集设备110的数据输出结果可以为一动态影片。该动态影片可以以一定帧数播放血管在所研究时间段内的连续状态变化情况。
所述血管影像资料可以是经过血管造影增强的图像。在一些实施例中，所述数据采集设备110输出的影像可能包含血管及其他身体组织。所述其他身体组织可以包括肌肉组织、骨骼、身体器官等部位。所述血管及其他身体组织可能相互交叉。为了去除其他身体组织对血管研究的影响，可以对数据采集设备110的输出影像进行血管造影增强处理以剥离出血管的部分。
所述载入的血管影像资料可以用来构建血管模型。所述血管模型可以为一个三维的血管模型。如果载入的是若干组不同时相的血管影像资料，则可以构建出若干组不同时相的三维血管模型。所述模型的构建可以是根据一系列血管截面影像来构建，或根据血管电子数据来构建。在一些实施例中，载入的数据为一系列的血管不同截面的影像，则可以根据各个截面中血管的轮廓以及该截面对应的坐标在空间中将这些血管截面进行叠加并拟合成一个完整的血管模型。在一些实施例中，所述血管模型的构建是根据电子数据来构建的。例如所载入的血管数据为数据采集设备110输出的血管上不同体素的坐标，则血管模型建立过程可以是将这些体素的坐标在空间中进行排布，并拟合成一个完整的血管模型。在一些实施例中所述血管模型可以是一个动态的三维影像。例如数据采集设备110输出的数据是血管上某个体素坐标在某个时间段内的连续变化信息。则血管模型建立过程可以是将各个体素坐标进行拟合并连续播放，从而生成一个具有一定帧数的连续三维影像模型。
血管提取单元420可以提取血管模型中的某一部分血管。所述一部分血管可以是一簇血管，一根血管或一根血管的某一段。在一些实施例中，造影的对象是心脏血管。心脏的血管可分为冠脉(冠状动脉)和静脉。针对对象的不同症状，医生可能会判断对象的心脏冠脉或静脉发生病变，则此时需要将所建立的血管模型中的冠脉和静脉进行剥离以利于医生进行诊断。又例如，在一些实施例中，医生在数据采集设备110的输出结果中已经有初步判断某一血管可能会存在异常，则在血管模型建立之后，可以利用血管提取单元420将该部分可能产生病变 的血管提取出来进行具体分析。再例如，在一些实施例中，建立后的血管模型上的某个局部血管段存在明显异样，则可以将该部分血管段提取出来进行具体分析。
血管分块单元430可以将血管进行分块。在一些实施例中，所建立的血管模型或提取出的部分血管模型在进行进一步的分析前需要将其分块成小的血管单元(也称为血管切片)。通过对各个小的血管单元分别进行研究分析，再将分别的研究分析结果按照其分块算法进行整合，则可以得到所研究血管段的整体分析结果。在一些实施例中，所述血管分块单元430可以沿血管延伸方向将血管壁分割成若干段曲面。在一些实施例中，所述血管分块单元430可以垂直于血管中心线将所研究血管段分割成若干空心圆柱段。所述血管中心线是沿着血管延伸方向，位于血管中心的假想的线条。所述空心圆柱段空心部分为血液流经的部分。所述分块可以是均匀的，或不均匀的。在一些实施例中，可以对于整段待研究血管模型进行均匀的划分。在一些实施例中，可能有部分血管是更感兴趣的部分，则可以将该部分血管做相对精细的分割(例如，分割成相对较小或较短的块或血管切片)，而其他相对次重要的部分血管可以进行相对粗糙的分割(例如，分割成相对较大或较长的块或血管切片)。
血管匹配单元440可以将不同时相的同一段血管进行匹配。在一些实施例中，载入处理模块320的是若干组不同时相的同一段血管的影像资料。在建立血管模型之后，若要对该段血管上的同一处在不同时相时的状态进行研究的话，则需要将不同时相血管模型进行匹配，找到不同时相血管模型上该段血管上的该处。所述匹配可以是对某块血管进行匹配，或是对血管上的点进行匹配。例如，在建立血管模型的时候就对血管上的体素进行编号，则在生成不同时相血管模型之后，相同编号的体素对应的区域可以被认为是匹配的区域。
计算单元450可以用来计算血管的状态变化。所述血管的状态变化可以包括血管上点的位移，血管在一段时间内的应变和/或应力变化，血管在某一时相的应变和/或应力分布等。在一些实施例中，该计算单元450可以分别计算上述血管切片的状态，并且可以将计算的结果按照血管切片的切割算法进行重新整合，得到整个血管的状态情况。在一些实施例中，针对不同时相的血管模型，该计算单元450可以分别两两计算血管模型以得出所计算的两个血管在对应状态下的变化。所述两两计算是指计算相邻的两个期相(也称为“时相”)血管模型 上参考点状态的变化。例如，计算第一期相和第二期相血管模型上参考点的变化，计算第二期相和第三期相血管模型上参考点的变化。该计算单元450还可以将两两计算的血管状态变化情况根据期相顺序整合起来，得到连续的血管状态变化情况。例如，将第一期相至第二期相的参考点变化与第二期相至第三期相的参考点变化情况进行整合，则可以得到第一期期相至第三期相时间内该参考点的动态变化情况。依次类推，可以得到完整心动周期内的血管模型上每个参考点的动态变化情况。
需要注意的是，以上对于处理模块320的描述，仅为描述方便，并不能把本申请限制在所举实施例范围之内。可以理解，对于本领域的技术人员来说，在了解该系统的原理后，可能在不背离这一原理的情况下，对各个模块进行任意组合，或者构成子系统与其他模块连接，对实施上述方法和系统的应用领域形式和细节上的各种修正和改变。例如在一些实施例中，数据载入单元410的血管模型构建功能可以由一个血管模型构建子单元来实现。在一些实施例中，血管提取单元420和血管分块儿单元430可以合并为一个获得血管切片的单元。
图5是根据本申请的一些实施例所示的一个分析模块230的示意图。所述分析模块340可以包括一个计算结果比对单元510、一个结果生成单元520和一个结果传输子单元530。所示各个单元彼此之间可以直接(和/或)间接连接。
计算结果对比单元510可以将计算单元450对血管状态的计算结果与一个参考结果进行比对并生成一个比对结论。所述参考结果可以是存储在存储模块330中的数据，可以是存储在网络180中的数据，或用户自行输入的数据。在一些实施例中，所述研究的血管状态参考结果及相关比对结论可以存储在一个表中。例如，所述血管状态为血管应力时，所述参考结果可以为一个血管应力范围与危险程度的对应关系。所述危险程度可以分为正常、预警、危险、极度危险等。在一些实施例中，用户可以根据临床经验进行手动输入所述对应关系。在一些实施例中，所述对比可以是同一对象不同时期的血管状态的计算结果对比。例如，某对象早晨的血管状态计算结果，中午的血管状态计算结果和晚上的计算结果可以通过计算结果对比单元510进行对比并得出该对象在一天当中血管的状态变化，可以帮助医生了解该对象在一天的什么时候是血管比较容易出现危险的时候。
结果生成单元520可以将计算结果比对单元510得出的结论生成不同形式的呈现方式。所述呈现方式可以是统计图、统计表、固定格式的文档和音频等。所述结果呈现方式可以通过交互设备140以呈现给用户。所述结果生成单元520可以包含一个图生成子单元521、一个表生成子单元522、一个文本报告生成子单元523、一个音频报告生成子单元524和一个视频报告生成子单元525。所述各个单元可以彼此直接(和/或间接)连接。
图生成子单元521可以生成一系列的统计图，或彩色和/或灰度的血管模型图等。所述统计图可以包括曲线图、折线图、饼图和直方图等统计图。例如，所述统计图可以是某处血管在一个心动周期内的应力变化曲线图。所述彩色和/或灰度的血管模型图可以用颜色深浅和/或灰阶来表示某段血管上的状态参数的分布情况。例如，可以用较深的颜色来表示某段血管上承受应力较大的部分。该部分可以被认为是有破裂风险的部分。
表生成子单元522可以生成一系列的数据对比表格。例如，在一些实施例中，所述表格的列项目可以是一系列血管的编号或者是血管切片的编号，行项目可以是血管或血管切片的状态参数类型，如位移，应变，应力等。对应状态参数类型的参考值也可以被显示在表中并以此为根据给出当前血管或血管切片的预警结论。所述预警结论可以是正常、预警、危险和极度危险等。
文本报告生成子单元523可以将计算结果对比单元510得出的结论以及计算单元450的计算结果生成一定格式的文字描述。所述一定格式的文字描述可以是预先设计好的文本。该文本在一些固定的位置根据计算结果及对比结论记性填充或修改。例如，预先设计好的文本格式可以为“…号血管…处在一个心动周期内的最大应变为…，最大应力为…，该处血管的参考正常最大应力为…，该出血管存在破裂的风险较低/较高/极高”。
音频报告生成子单元524可以根据对比结论生成语音报告和/或发出相应的预警声音。在一些实施例中，所述语音报告可以是在预先录入的语音文件中的对应部分加入对比结果及计算结果。例如，将上述的文本格式内容录制为语音模板，在特定位置播报对比结果或计算结果。在一些实施例中，所述预警声音可以是根据对比结果中的预警结论来发出。例如，当预警结论为危险或极度危险时，交互设备140可以发出不同频率的蜂鸣声以提醒用户。
视频报告生成子单元525可以根据将计算结果对比单元510得出的结论以及计算单元450的计算结果生成一个动画或视频。作为示例，视频报告生成子单元525可以将计算单元450的计算结果生成一个动画，展示一段时间内(例如，一个或多个心动周期内)血管、血液流动、或血管状态的变化。在一些实施例中，一个视频报告可以包括音频、文字、图标。作为示例，一个展示一段时间内(例如，一个或多个心动周期内)血管、血液流动、或血管状态的变化的动画可以与一个音频讲解，或文字说明相配合，提供动画中在不同时相的血管、血液流动、或血管状态的一个或多个参数(例如，血压、应力等)。
结果传输子单元530可以生成的不同结果呈现方式传输给传输模块350，进而传送至不同的用户终端，也可以直接传送至不同的用户终端。在一些实施例中，结果生成单元520生成的结果、计算单元450的计算结果等可以通过网络180发送至用户的终端。所述用户终端可以是电脑、手机、显示装置、打印机、传真等设备。所述结果可以同时传送至多个用户终端。在一些实施例中，多个终端相对于数据采集设备110或处理设备120可以是当地的或远程的。例如，某对象有多个会诊医生，该用户的血管状态结果可以同时发送至该对象、其家属、监护人、所述多个会诊医生等的终端。
需要注意的是，以上对于分析模块340的描述，仅为描述方便，并不能把本申请限制在所举实施例范围之内。可以理解，对于本领域的技术人员来说，在了解该系统的原理后，可能在不背离这一原理的情况下，对各个模块进行任意组合，或者构成子系统与其他模块连接，对实施上述方法和系统的应用领域形式和细节上的各种修正和改变。例如在一些实施例中，结果生成单元520中的部分个子单元可以省略。
图6是根据本申请的一些实施例所示的一个血管提取单元420的示意图。所述血管提取单元420可以包含一个血管自动分割子单元610和一个血管半自动分割子单元620。所述分割是指将构建的血管模型进行分割，可以包含将血管与人体其他组织进行分割，将某一类血管(如冠脉)与其他种类血管进行分割，将血管上的某一部分与其他部分进行分割(如将血管靠近心脏的部分进行分割)等。血管分割可以基于一种或多种算法。血管分割算法可以包括阈值法、区域生长法、基于能量函数的方法、水平集方法、区域分割和/或合并、边缘跟踪分割法、 统计模式识别方法、均值聚类分割法、模型法、基于可变形模型的分割法、人工神经网络方法、最小路径分割法、跟踪法、基于规则的分割法、耦合表面分割法等，或者上述分割方法的任意组合。
血管自动分割子单元610可以自动将构建的血管模型进行分割处理。在一些实施例中，所述的自动分割算法可以是预先存储在计算设备内，例如，图2A所述的计算设备内。例如，针对某一类血管分析，需要单独对一簇中的每一根血管进行单独分析，则在构建完血管模型后可以自动将模型中的单根血管进行分割。在一些实施例中，所述的自动分割算法可以是根据成像血管的部位确定。例如，在心脏病对象的诊断过程中，医生可能会希望知道该对象心脏冠状动脉的应力分布情况。则在构建该对象心脏部分血管的模型之后，可以自动将血管模型中的冠状动脉与静脉进行分割。在一些实施例中，所述的自动分割算法可以是根据诊断的目的确定。例如，在一些疾病诊断中医生可能会需要知道毛细血管的分布情况，则对于构建的血管模型，相对较粗的血管部分和相对较细的血管部分可以被分割。所述的自动分割算法可以存储在存储模块330中，且可以在需要调用的时候通过交互设备140由用户进行选择。所述自动分割算法还可以通过网络180进行升级优化。例如，设备生产厂商可能会定期或不定期地将血管分割的方法进行优化或提供新的血管分割方法，并提供相关数据包供用户下载进行更新。
所述血管半自动分割子单元620可以接受用户指令来辅助进行血管分割。在一些实施例中，医生可能会根据某些特殊的病例需要进行特殊化的血管分割操作，且该血管分割操作无法或不合适通过自动分割的方法实现。在构建完血管模型后，医生可根据对象具体症状进行手动分割。例如，医生可以通过交互设备140将构建完的血管模型中比较明显的可能出现病变的部位挑选出来进行分割。所述挑选可以是指定血管模型上的某一个区域，或者某一段血管。所述指定某一段血管可以是手动选择某根血管上的两个点以分割两个点之间的血管。所述手动分割也可以是建立在自动分割的基础上的。例如，在对心脏冠脉的诊断中，可以由血管自动分割子单元610将冠脉先分割出来，再由医生就分割出的冠脉上可能出现病变的部分进行进一步的分割。在一些实施例中，自动分割出的血管可能由于自动分割算法优化不足，或计算设备计算能力不足等原因存在一定的误差，且这些误差可能会影响到医生对疾病的诊断，则需要通过手动方法对自动分割后的血管 进行修饰。例如，心脏冠脉通过自动分割方法分离出来后仍有部分静脉没有与冠脉没有完全分割，则可以通过血管半自动分割单元620手动将这部分静脉分割出去。又例如，在分析某个器官或组织毛细血管的诊断中，为了防止或减少计算设备计算误差，可以将计算设备判断毛细血管的阈值调整为较宽以避免或减少漏掉部分毛细血管。在通过自动分割方法分割后，可能还有部分较粗血管没有被分割干净。此时可以通过血管半自动分割子单元620将这部分较粗的血管分割出去。
图7是根据本申请的一些实施例所示的一个血管分块单元430的示意图。所述血管分块单元430可以包含一个中心线提取子单元710和一个血管轮廓标定子单元720。所述血管分块可以是将某根血管以某一分块方法分成可以进行数值分析的单元。例如，将血管模型按照其体素个数分块成若干部分。又例如，将血管以垂直于血管中心线的方向切割，分成若干块血管段。再例如，以一定尺寸在血管壁上截取一定表面积大小对应的血管壁等。
中心线提取子单元710可以提取某段血管的中心线。在一些实施例中，血管中心线可以指位于血管内部的沿着血管走向的一条假想的线。血管中心线可以包括血管中一个或多个像素点(或体素点)的集合。血管可以包括血管边界线及血管边界线以内的像素点(或体素点)的集合。在一些实施例中，血管中心线可以包括血管中心或靠近血管中心的像素点(或体素点)的集合或组成的一条线。在一些实施例中，血管中心线可以包括一个或多个血管端点。所述血管中心线为所述端点之间的一条路径。在一些实施例中，所述血管中心线的确定方法可以为将血管平行切割成一定数量的血管段，确定每个血管中心的一个中心点，使得该点距离血管壁的统计学方差最小。将各个血管段的中心点用曲线进行拟合。如果切割的血管段数量足够多，则拟合的曲线可以认为是该段血管的中心线。在一些实施例中，关于血管中心线提取的示例性方法可以参考2016年8月30日提交的申请号为PCT/CN2016/097294的国际申请，其内容以引用的方式被包含于此。获得该段血管的中心线之后，可以将其平均分割成若干段中心线段，使得每段中心线段都对应着一段血管壁。将该部分血管壁作为血管分割模块430输出的血管切片。当中心线段选择的足够小时，血管切片的沿血管方向的厚度可以不予考虑，则所述血管切片为一只考虑血管截面形状的环状结构。
血管轮廓标定子单元720可以对血管切片进行离散化处理。该血管轮廓 标定子单元720可以确定血管切片的轮廓。由于血管切片的血管壁有一定的厚度，所以血管壁的轮廓可以近似为血管内壁的轮廓，血管外壁的轮廓、或者其他可以描述血管切片环状结构的封闭曲线。在一些实施例中，由于血管切片的轮廓为一连续曲线，为了利于分析，需要对其轮廓曲线进行离散化处理。例如，在所述血管轮廓曲线上每隔一段距离设置一个参考点。所述参考点的分布可以是均匀的，或不均匀的。例如，在一些实施例中，对于心脏血管的研究可能主要关注血管近心肌一侧的状态情况，则在血管切片近心肌的一侧可以设置较多的参考点，而另外一侧设置较少的参考点。在一些实施例中，所述参考点可以被标定以进行不同时相同一血管段的匹配。例如，将某个血管切片上的某个点标记为“M-n”，表示该点为第M个血管切片上的第n个参考点。在分析不同时相同一血管段状态时，可将相同标记的参考点进行匹配。所述参考点的标记方法可以根据血管模型所处空间的坐标进行标记，例如以距离原点的距离远近进行标记等。所述参考点的标记也可以根据血管种类特征进行。例如，研究覆盖在心肌表面的血管时，可以以最靠近心肌的参考点设为初始参考点，以顺时针或者逆时针的方向对参考点进行标定。
图8是根据本申请的一些实施例所示的一个计算单元450的示意图。所述计算单元450可以包含一个位移确定子单元810，一个应变确定子单元820和一个应力确定子单元830。所示各个单元可以彼此相互独立也可以相互关联。例如，可以根据确定后的位移来确定应变；可以根据确定后的应变来确定应力。
位移确定子单元810可以确定所述参考点的位移。所述位移为不同的血管模型中对应参考点在在同一模型空间中的位移。在一些实施例中，所述位移可以是相对位移。例如，在第一个时相的血管模型中某参考点的空间坐标为A。在第二个时相的血管模型中对应的参考点的空间坐标为B，则该参考点从第一时相到第二时相的相对位移为从A点指向到B的的一个空间向量。以此类推，所述两个时相的血管模型上的多个参考点的位移都可以被确定。所述位移计算结果可以被存储到存储模块330中。应变确定子单元820可以确定所述对应参考点的应变。在一些实施例中，在确定了对应参考点之间的位移之后，可以据此确定相应的应变，例如根据格林应变张量式等。所述应变结果可以被存储到存储模块330中。应力确定子单元830可以确定所述对应参考点所受的应力。在一些实施例中， 所述应力的确定可以根据应变与应力之间的对应关系来确定。例如，可以通过查表的方法查出所研究血管段的弹性模量，根据弹性模量和应变来确定应力。所述应力结果可以被存储在存储模块330中。
图9是根据本申请的一些实施例所示的一个获取血管应力应变状态的示例性流程图。血管应力应变状态获取流程可以包括获取多时相血管数据并建立血管模型910，提取血管模型中感兴趣的部分920，在提取的血管模型上设置若干参考点930，匹配不同期相的对应参考点940，根据多期相的数据计算参考点位移950和根据参考点位移计算血管的应变与应力960。
在910中，可以获取多时相血管数据并建立血管模型。在一些实施例中，获取多时相血管数据操作可以被数据载入单元410执行。所述血管数据可以包含数据载入单元410可以接收的血管影像资料。所述的血管数据可以是血管造影图片，或电子数据。关于血管数据类型举例的相关描述可以参考数据载入单元410中的描述。在一些实施例中，所载入的血管数据可以为一个若干组不同时相的血管状态对应的影像资料。例如，所述若干组不同时相可以构成一部分或完整的心动周期。所述一部分心动周期可以是心脏舒张过程所对应的时间段，或心脏收缩过程所对应的时间段。所述若干组不同时相的血管状态对应影像资料可以反映该部分血管在对应时段内的状态变化。所述若干组的影像资料的组数可以由用户决定或设备性能决定。例如，所述组数可以小于或等于用来成像的数据采集设备110在一个心动周期的时间段内可以采集或生成的影像资料组数上限。又例如，所述组数可以根据医生对疾病诊断难度的判断来决定；诊断难度越大，需求的组数可以越多。所述血管影像资料可以是经过血管造影增强的图像。在一些实施例中，所述数据采集设备110输出的影像可能包含血管及其他身体组织。所述其他身体组织可以包括肌肉组织、骨骼、身体器官等部位。所述血管及其他身体组织可能相互交叉。为了去除其他身体组织对血管研究的影响，可以对数据采集设备110的输出影像进行血管造影增强处理以剥离出血管的部分。在一些实施例中，建立血管模型的操作也可以由数据载入单元410执行。所述血管模型可以为三维血管模型。如果载入的是多时相的血管数据，则可以建立对应的多时相的三维血管模型。所述模型构建的方法可以参考数据载入模块410中的描述。
在920中，可以提取血管模型中感兴趣的部分。在一些实施例中，该操 作可以被血管提取单元420执行。在一些实施例中，所述感兴趣的部分可以是根据对象的症状来确定的。例如，某对象在心脏收缩的时候会感觉心绞痛，则医生可能会判断对象心脏的冠脉部分可能存在病变，则可以将冠脉血管确定为感兴趣的部分。又例如，对象可能感觉某些毛细血管分布密集的地方不适，则医生可能会判断该对象的该部分毛细血管存在病变而将该部分确定为感兴趣的部分。所述感兴趣的部分可以由用户通过交互设备140进行输入。所述提取血管模型中感兴趣的部分为血管提取单元420可执行的将血管进行分割的功能。所述提取可以是自动提取和/或半自动提取。关于血管分割的方法可以参考血管提取单元420及其中的血管自动分割子单元610、血管半自动分割子单元620中的描述。该提取操作的操作结果可以为获取一根血管或一根血管上的一段。
在930中，可以在提取的血管模型上设置若干参考点。在一些实施例中，该设置参考点的操作可以由血管分块单元430及其中的血管轮廓标定子单元720执行。为了分析多时相血管模型的变化，可以选择通过研究不同相期血管模型上的对应点变化来实现。在一些实施例中，可以先将之前提取的血管模型进行分块，所述分块可以参考血管分块单元430中描述的分块方法。在一些实施例中，可以将血管沿着血管中心线的垂直方向进行切片，得到一系列血管段。当切片足够多时，可不考虑血管段沿中心线方向的厚度。在一些实施例中，可以由血管轮廓标定子单元720对血管段的环状轮廓进行提取，并在其轮廓上设置参考点。所述设置参考点的方法可以参考血管轮廓标定子单元720中的描述。
在940中，可以匹配不同期相的对应参考点。在一些实施例中，该匹配操作可以由血管匹配单元440执行。对于不同时相的血管模型，其上的参考点需要进行匹配后才能进行一一对比研究。例如，第一期相血管模型中的某参考点不能和第二期相血管模型中的其他参考点进行比较。所述的参考点匹配操作可以参考血管匹配单元440中的描述。
在950中，可以根据多期相的数据计算参考点位移。在一些实施例中，该参考点位移计算的操作可以由位移确定子单元810来执行。在一些实施例中，参考点的位移为相邻期相的相对位移。例如，血管上某点在第一期相血管模型中的参考点为A1，在第二期相血管模型中的参考点为A2，在第三期相血管模型中的参考点为A3。则计算的参考点位移分别为从A1到A2的位移和从A2到A3 的位移，而不会计算从A1到A3的位移。以此类推，得到该参考点在多期相血管模型中的连续的位移变化信息。所述的位移计算操作可以参考位移确定子单元810中的描述。
在960中，可以根据参考点位移计算血管的应变与用力。在一些实施例中，该应变与应力可以由应变确定子单元820和应力确定子单元830来执行。根据点的位移与应变应力之间的相互关系，可以根据950中的连续的位移变化信息确定连续的应变变化情况和对应的应力变化情况。所述确定应变应力的方法可以参考应变确定子单元820和应力确定子单元830中的描述。
需要注意的是，以上对于获取血管应力应变状态的描述，仅为描述方便，并不能把本申请限制在所举实施例范围之内。可以理解，对于本领域的技术人员来说，在了解该系统的原理后，可能在不背离这一原理的情况下，对各个模块进行任意组合，或者构成子系统与其他模块连接，对实施上述方法和系统的应用领域形式和细节上的各种修正和改变。例如步骤920可以被省略。在一些实施例中，可以对建立的血管模型整体进行设置参考点的操作。这可能会带来计算量的上升，但同样可以继续执行930之后的操作。
图10是根据本申请的一些实施例所示的一个在提取的血管模型上设置参考点的示例性流程图。在提取的血管模型上设置参考点的流程可以包括将提取的血管模型分段为若干切片1010，提取血管切片轮廓1020和基于血管轮廓设置若干参考点1030。
在1010中，可以将提取的血管模型分段为若干切片。在一些实施例中，该切片操作可以由血管分块单元430执行。在一些实施例中，所述分段可以是将血管沿垂直血管延伸方向切割成一定数量的血管段，所述切片方法可以参考分块单元430中的描述。
在1020中，可以提取血管切片的轮廓。在一些实施例中，该轮廓提取的操作可以由血管轮廓标定子单元720执行。所述血管切片的轮廓可以是血管内壁的轮廓，血管外壁的轮廓等可以描述血管切片环状结构的封闭曲线。所述血管切片轮廓的确定方法可以参考血管轮廓标定子单元720中的描述。
在1030中，可以基于血管轮廓设置若干参考点。在一些实施例中，该参考点设置操作可以由血管轮廓标定子单元720执行。在一些实施例中，所述参考 点设置操作可以是将血管轮廓曲线进行离散化处理。所述离散化处理方法可以参考血管轮廓标定子单元720中的描述。在一些实施例中，所研究的血管为心肌表面血管，则参考点可以以靠心肌最近的点为初始点，逆时针或顺时针以此进行标定。
图11是根据本申请的一些实施例所示的一个将提取的血管模型分段为若干血管切片的示例性流程图。将提取的血管模型分段为若干血管切片的流程可以包括确定提取的血管模型的中心线1110，将中心线等距离划分为若干段1120和确定每段中心线对应的血管为血管切片。在一些实施例中，上述各操作可以由血管分块单元430来执行。
在1110中，可以确定提取的血管模型的中心线。在一些实施例中，该确定中心线的操作可以由中心线提取子单元710来执行。在一些实施例中，血管中心线可以是指位于血管内部的沿着血管走向的一条假想的线。所述确定血管中心线的方法可以参考中心线提取子单元710中的描述。
在1120中，可以将中心线等距离划分成若干段。在一些实施例中，该划分操作可以由中心线提取子单元710来执行。所述等距离划分成若干段的数量可以由计算设备处理能力或处理结果要求精度来决定。在一些实施例中，中心线的划分是为了将提取的血管模型分成若干单元，分别进行分析后再整合。如果单元数量过多，可能导致计算量增大，处理时间延长，但相应的整合后的处理结果精度可能会得到提高。所以用户可以根据具体诊断需要，手动的选择需要划分的单元的数量。例如，某急诊对象在手术室中急需血管状态诊断结果，则医生可能会希望尽快得到处理结果，相应的划分数量可以减少。又例如某对象经过多次诊断仍然不能够查出病因，则医生可能会希望得到更精确的处理结果，相应的划分数量可以增加。
在1130中，可以确定每段血管中心线对应的血管为血管切片。在一些实施例中，该确定血管切片的操作可以由中心线提取子单元710来执行。在划分完血管中心线段后，可以根据该中心线段确定其对应的血管壁范围。在一些实施例中，可以以一个始终垂直于该中心线段的平面从该中心线段的一端扫描至另一端，该平面涉及的血管壁则被确定为该段中心线段对应的血管壁。确定的该段血管壁可以视为血管切片。当中心线段选择的足够小时，血管切片的沿血管方向的厚度 可以不予考虑，则所述血管切片为一只考虑血管截面形状的环状结构。
图12是根据本申请的一些实施例所示的一个在血管模型上设置参考点的示意图。在A中，数据载入单元410根据数据采集设备的影像资料构建了一个三维的血管模型，且该模型包含了血管1和血管2。在一些实施例中，血管1可以是心脏冠脉，血管2可以是心脏静脉。对血管1进行研究分析则需要将血管1和血管2进行分离。在B中，血管提取单元420将血管1与血管2进行了分割并将血管1提取了出来。所述分割可以是自动的将心脏冠脉与静脉进行分割，或用户手动在A中的血管模型中标识出血管1并进行提取。为了对提取出的血管1进行研究，还可以将血管1进行分块，该分块操作可以由血管分块单元430来完成。根据之前描述的血管分块方法，血管1可以被分块成若干的血管段。虚线框α中的血管段可以是分块后的若干血管段中的一个。如果虚线框α的宽度足够小，则可以不考虑该血管段沿血管延伸方向的厚度，虚线框α中的血管段为一血管切片。B中虚线框内的血管切片截面为一个闭合的环状结构。在C中，可以由血管轮廓标定子单元720提取该血管切片的轮廓，所述轮廓的提取方法可以参考血管轮廓标定子单元720中的描述。提取后的轮廓为一个环状曲线，为了便于分析，可以将该曲线进行离散化。在D中，所述曲线被等距离的插值成若干参考点。所述参考点的个数可以是50到5000中的任意整数，例如100或200。在一些实施例中，所述参考点的个数可以根据参考点之间距离决定。例如在设置参考点时，可以确定两个参考点之间的距离为某一固定值，则参考点的个数根据该固定值的选取来决定。在确定完参考点后，还需对参考点进行标定。例如，D中将最下方的点设置为起始参考点1，以逆时针的方向依次对参考点进行标定，直至最后一个参考点标定完毕。在一些实施例中，针对某种类型的血管，如人体器官表面的血管，所述参考点1的确定可以是该血管切片最贴近该人体器官表面的点。在一些实施例中，所述参考点1的确定可以是最接近于血管模型所在空间坐标系原点或某个轴的点。
图13是根据本申请的一些实施例所示的一个根据不同期相血管模型确定参考点位移的示意图。图中T1和T2为两个不同期相同一个血管切片图，且两个血管切片都经过了轮廓确定与参考点设置。该血管切片从T1到T2的过程中由于某种原因导致其在横向宽度上有所扩张，且对应参考点都发生了一定程度 的位移。在T1和T2中有对应的参考点N1和N2。可以将N1和N2的坐标放到一个三维坐标系M中(省略z轴)，则可以得到参考点N1到N2的位移。该位移可以用一个从N1指向N2的向量表示。
根据以上示意图，可以确定该血管切片上所有参考点从T1期相到T2期相的位移。每个血管切片都依次方法则可以得到提取的血管上所有的参考点从T1到T2期相的位移，进而根据位移与应变、应力的关系可以确定该提取的血管从T1期相到T2期相的应变和应力。依次的计算T2到T3期相，T3到T4期相，直至一个完整的心动周期，则可以确定在该时间段内的血管上各个部分的应变应力变化情况。
图14是根据本申请的一些实施例所示的一个输出评估血管状态结果的示例性流程图。所述输出评估血管状态结果可以包含获取应力或应变数据1410，与参考血管应力或应变数据比较1420，根据比较结果评估血管状态1430和输出评估血管状态结果1440。在一些实施例中，上述步骤可以通过分析模块340来执行。
在1410中，可以获取应力或应变数据。所述应力或应变数据可以是960中获取的应力或应变数据。在一些实施例中，对于某一血管模型上的某个参考点在不同期相下所承受的应力或应变可能是不同的。反应该参考点处的应力或应变特性的数据可以是该点在一段时间内，或一个或多个心动周期承受的最大应力或应变的数据，或在一段时间内或一个或多个心动周期应力或应变数据的累积平均值。例如，在诊断某些血管疾病时，血管可能存在一个能承受的最大应力，则该点在不同期相中承受的最大应力或应变值可以作为反映该点应力或应变特性的数据。又例如，在一些实施例中，血管上的某参考点长时间的承受一定程度的应力或应变，则可能经过时间的累积该参考点出可能存在破裂的风险。此时该点在不同期相应力或应变数据的累积平均值可以作为该点的应力或应变特性数据。
在1420中，可以与参考血管应力或应变数据比较。在一些实施例中，所述比较操作可以由计算结果对比单元510来执行。所述比较可以是将1410中的参考点应力应变特性数据与参考血管应力或应变数据进行比较。所述的参考血管应力或应变数据可以是存储在存储模块330中的数据，通过网络180获取的数据，或用户输入的数据。在一些实施例中，所述参考血管应力或应变数据可以是 计算结果对比单元510中描述的血管状态参考结果。
在1430中，可以根据比较结果评估血管状态。所述血管应力应变特性数据与参考数据的比较结果可以对应一个比较结论，例如正常、预警、危险、极度危险等。在一些实施例中，所述参考数据可能是一些数据区间，分别对应相应的比较结论。当所述血管应力或应变特性数据落在某个区间内时，则对应相应的比较结论。所述的评估血管状态结论可以通过结果生成单元520进行处理，生成统计图、统计表、固定格式的文档和音频等文件。相关的生成方式可以参考结果生成单元520中的描述。
在1440中，可以输出评估血管状态结果。在一些实施例中，所述输出结果的操作可以由结果传输子单元530来执行。所述结果的输出方式可以参考结果传输子单元530中的描述。
上文已对基本概念做了描述，显然，对于本领域技术人员来说，上述发明披露仅仅作为示例，而并不构成对本申请的限定。虽然此处并没有明确说明，本领域技术人员可能会对本申请进行各种修改、改进和修正。该类修改、改进和修正在本申请中被建议，所以该类修改、改进、修正仍属于本申请示范实施例的精神和范围。
同时，本申请使用了特定词语来描述本申请的实施例。如“一个实施例”、“一实施例”、和/或“一些实施例”意指与本申请至少一个实施例相关的某一特征、结构或特点。因此，应强调并注意的是，本说明书中在不同位置两次或多次提及的“一实施例”或“一个实施例”或“一替代性实施例”并不一定是指同一实施例。此外，本申请的一个或多个实施例中的某些特征、结构或特点可以进行适当的组合。
此外，本领域技术人员可以理解，本申请的各方面可以通过若干具有可专利性的种类或情况进行说明和描述，包括任何新的和有用的工序、机器、产品或物质的组合，或对他们的任何新的和有用的改进。相应地，本申请的各个方面可以完全由硬件执行、可以完全由软件(包括固件、常驻软件、微码等)执行、也可以由硬件和软件组合执行。以上硬件或软件均可被称为“数据块”、“模块”、“引擎”、“单元”、“组件”或“系统”。此外，本申请的各方面可能表现为位于一个或多个计算机可读介质中的计算机产品，该产品包括计算机可读程序编码。
计算机可读信号介质可能包含一个内含有计算机程序编码的传播数据信 号，例如在基带上或作为载波的一部分。该传播信号可能有多种表现形式，包括电磁形式、光形式等等、或合适的组合形式。计算机可读信号介质可以是除计算机可读存储介质之外的任何计算机可读介质，该介质可以通过连接至一个指令执行系统、装置或设备以实现通讯、传播或传输供使用的程序。位于计算机可读信号介质上的程序编码可以通过任何合适的介质进行传播，包括无线电、电缆、光纤电缆、RF、或类似介质、或任何上述介质的组合。
本申请各部分操作所需的计算机程序编码可以用任意一种或多种程序语言编写，包括面向对象编程语言如Java、Scala、Smalltalk、Eiffel、JADE、Emerald、C++、C#、VB.NET、Python等，常规程序化编程语言如C语言、Visual Basic、Fortran 2003、Perl、COBOL 2002、PHP、ABAP，动态编程语言如Python、Ruby和Groovy，或其他编程语言等。该程序编码可以完全在用户计算机上运行、或作为独立的软件包在用户计算机上运行、或部分在用户计算机上运行部分在远程计算机运行、或完全在远程计算机或服务器上运行。在后种情况下，远程计算机可以通过任何网络形式与用户计算机连接，比如局域网(LAN)或广域网(WAN)，或连接至外部计算机(例如通过因特网)，或在云计算环境中，或作为服务使用如软件即服务(SaaS)。
此外，除非权利要求中明确说明，本申请所述处理元素和序列的顺序、数字字母的使用、或其他名称的使用，并非用于限定本申请流程和方法的顺序。尽管上述披露中通过各种示例讨论了一些目前认为有用的发明实施例，但应当理解的是，该类细节仅起到说明的目的，附加的权利要求并不仅限于披露的实施例，相反，权利要求旨在覆盖所有符合本申请实施例实质和范围的修正和等价组合。例如，虽然以上所描述的系统组件可以通过硬件设备实现，但是也可以只通过软件的解决方案得以实现，如在现有的服务器或移动设备上安装所描述的系统。
同理，应当注意的是，为了简化本申请披露的表述，从而帮助对一个或多个发明实施例的理解，前文对本申请实施例的描述中，有时会将多种特征归并至一个实施例、附图或对其的描述中。但是，这种披露方法并不意味着本申请对象所需要的特征比权利要求中提及的特征多。实际上，实施例的特征要少于上述披露的单个实施例的全部特征。
一些实施例中使用了描述成分、属性数量的数字，应当理解的是，此类 用于实施例描述的数字，在一些示例中使用了修饰词“大约”、“近似”或“大体上”等来修饰。除非另外说明，“大约”、“近似”或“大体上”表明所述数字允许有±20％的变化。相应地，在一些实施例中，说明书和权利要求中使用的数值参数均为近似值，该近似值根据个别实施例所需特点可以发生改变。在一些实施例中，数值参数应考虑规定的有效数位并采用一般位数保留的方法。尽管本申请一些实施例中用于确认其范围广度的数值域和参数为近似值，在具体实施例中，此类数值的设定在可行范围内尽可能精确。
最后，应当理解的是，本申请中所述实施例仅用以说明本申请实施例的原则。其他的变形也可能属于本申请的范围。因此，作为示例而非限制，本申请实施例的替代配置可视为与本申请的教导一致。相应地，本申请的实施例不仅限于本申请明确介绍和描述的实施例。

Claims (19)

  1. 一种可以被至少一个包含处理器和存储器的设备执行的方法，所述方法包括：
    获取对应于一个血管的第一时相血管数据；
    获取对应于所述血管的第二时相血管数据；
    基于所述第一时相血管数据建立第一时相的血管模型；
    基于所述第二时相血管数据建立第二时相的血管模型；
    提取所述第一时相血管模型中感兴趣的部分；
    提取所述第二血管模型中所述感兴趣的部分；
    在所述第一时相血管模型中感兴趣的部分设置一个参考点；
    在所述第二时相血管模型中所述感兴趣的部分找到所述参考点；
    确定所述参考点的位移；以及
    根据所述参考点的位移确定所述参考点处的应力或应变。
  2. 权利要求1所述的方法，进一步包括：
    将所述应力或应变与参考数据进行比较；
    根据比较结果评估血管状态；以及
    将血管状态评估结果传输给用户。
  3. 权利要求2所述的方法，所述血管状态评估结果中的结果呈现形式为下列形式中的至少一种，包括图、表、固定格式的文字以及音频。
  4. 权利要求2所述的方法，所述参考数据存储在一个存储设备中。
  5. 权利要求2所述的方法，所述将血管状态评估结果传输给用户包括将血管状态评估结果传输到至少一个用户的用户终端。
  6. 权利要求2所述的方法，所述将所述确定的应力或应变与参考数据进行比较包括：
    确定所述参考点应力或应变的特征值；以及
    将所述特征值与所述参考数据进行比较。
  7. 权利要求6所述的方法，所述参考点的所述应力或应变的特征值包括所述参考点在不同时相中的最大应力或应变值。
  8. 权利要求6所述的方法，所述参考点的所述应力或应变的特征值包括所述参考点在不同时相中的应力或应变的平均值。
  9. 权利要求1所述的方法，所述在所述第一时相血管模型中感兴趣的部分设置一个参考点包括：
    将所述血管分段为若干血管切片；
    提取所述若干血管切片中一个血管切片的轮廓；以及
    基于所述血管切片的轮廓设置所述参考点。
  10. 权利要求9所述的方法，所述将血管分段为若干血管切片包括：
    确定所述血管的中心线；
    将所述中心线划分为若干中心线段；以及
    确定所述若干中心线段中的一段中心线段对应的血管段为所述若干血管切片中的一个血管切片。
  11. 权利要求9所述的方法，所述基于血管切片的轮廓设置所述参考点包括：
    在所述血管轮廓上均匀等间距设置一定数量的参考点；以及
    从所述一定数量的参考点中选取所述参考点。
  12. 权利要求11所述的方法，进一步包括在所述设置的参考点中选定一个初始点，以及从初始点开始以逆时针或顺时针的方向对所述一定数量的参考点依次编号。
  13. 权利要求1所述的方法，所述第一时相的血管模型包括心脏血管模型，所述 心脏血管模型包括冠脉和静脉，所述提取所述第一时相血管模型中感兴趣的部分包括自动提取所述心脏血管模型中的冠脉。
  14. 权利要求1所述的方法，所述提取所述第一时相血管模型中感兴趣的部分包括：
    从用户接收所述血管的两个端点的信息；以及
    根据所述接收到的血管的两个端点的信息，从所述血管中提取所述两个端点之间的血管段。
  15. 权利要求1所述的方法，所述确定所述参考点的位移包括确定相邻时相间的所述参考点的位移。
  16. 一种非暂时性的计算机可读介质，包括可执行指令，所述指令被至少一个处理器执行时，导致所述至少一个处理器实现一种方法，包括：
    获取对应于一个血管的第一时相血管数据；
    获取对应于所述血管的第二时相血管数据；
    基于所述第一时相血管数据建立第一时相的血管模型；
    基于所述第二时相血管数据建立第二时相的血管模型；
    提取所述第一时相血管模型中感兴趣的部分；
    提取所述第二血管模型中所述感兴趣的部分；
    在所述第一时相血管模型中感兴趣的部分设置一个参考点；
    在所述第二时相血管模型中所述感兴趣的部分找到所述参考点；
    确定所述参考点的位移；以及
    根据所述参考点的位移确定所述参考点处的应力或应变。
  17. 一个提取血管中心线的系统，包括：
    至少一个处理器，以及
    可执行指令，所述指令被至少一个处理器执行时，导致所述至少一个处理器实现一种方法，包括：
    获取对应于一个血管的第一时相血管数据；
    获取对应于所述血管的第二时相血管数据；
    基于所述第一时相血管数据建立第一时相的血管模型；
    基于所述第二时相血管数据建立第二时相的血管模型；
    提取所述第一时相血管模型中感兴趣的部分；
    提取所述第二血管模型中所述感兴趣的部分；
    在所述第一时相血管模型中感兴趣的部分设置一个参考点；
    在所述第二时相血管模型中所述感兴趣的部分找到所述参考点；
    确定所述参考点的位移；以及
    根据所述参考点的位移确定所述参考点处的应力或应变。
  18. 权利要求17所述的系统，进一步包括权利要求16所述的非暂时性的计算机可读介质。
  19. 一种系统包括：
    至少一个处理器，以及
    存储器，用来存储指令，所述指令被所述至少一个处理器执行时，导致所述系统实现的操作包括：
    获取对应于一个血管的第一时相血管数据；
    获取对应于所述血管的第二时相血管数据；
    基于所述第一时相血管数据建立第一时相的血管模型；
    基于所述第二时相血管数据建立第二时相的血管模型；
    提取所述第一时相血管模型中感兴趣的部分；
    提取所述第二血管模型中所述感兴趣的部分；
    在所述第一时相血管模型中感兴趣的部分设置一个参考点；
    在所述第二时相血管模型中所述感兴趣的部分找到所述参考点；
    确定所述参考点的位移；以及
    根据所述参考点的位移确定所述参考点处的应力或应变。
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