CN116779135A - Method, apparatus, computing device and medium for calculating fractional blood reserve - Google Patents
Method, apparatus, computing device and medium for calculating fractional blood reserve Download PDFInfo
- Publication number
- CN116779135A CN116779135A CN202210220910.5A CN202210220910A CN116779135A CN 116779135 A CN116779135 A CN 116779135A CN 202210220910 A CN202210220910 A CN 202210220910A CN 116779135 A CN116779135 A CN 116779135A
- Authority
- CN
- China
- Prior art keywords
- vascular
- vessel
- coarse
- fine
- blood vessel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 239000008280 blood Substances 0.000 title claims abstract description 26
- 210000004369 blood Anatomy 0.000 title claims abstract description 26
- 230000002792 vascular Effects 0.000 claims abstract description 69
- 210000004204 blood vessel Anatomy 0.000 claims abstract description 67
- 230000000004 hemodynamic effect Effects 0.000 claims abstract description 38
- 238000000547 structure data Methods 0.000 claims abstract description 36
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000004590 computer program Methods 0.000 claims description 31
- 238000002583 angiography Methods 0.000 claims description 8
- 230000017531 blood circulation Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 6
- 238000013215 result calculation Methods 0.000 claims description 4
- 239000012530 fluid Substances 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 15
- 230000009471 action Effects 0.000 description 10
- 238000004458 analytical method Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 238000003384 imaging method Methods 0.000 description 7
- 230000003902 lesion Effects 0.000 description 6
- 210000004351 coronary vessel Anatomy 0.000 description 5
- 238000007726 management method Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000013421 nuclear magnetic resonance imaging Methods 0.000 description 4
- 238000002600 positron emission tomography Methods 0.000 description 4
- 238000002603 single-photon emission computed tomography Methods 0.000 description 4
- 238000003325 tomography Methods 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 230000001413 cellular effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 238000002595 magnetic resonance imaging Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 210000004165 myocardium Anatomy 0.000 description 3
- 238000002601 radiography Methods 0.000 description 3
- 238000002604 ultrasonography Methods 0.000 description 3
- 208000031481 Pathologic Constriction Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000002591 computed tomography Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 208000037804 stenosis Diseases 0.000 description 2
- 230000036262 stenosis Effects 0.000 description 2
- 230000002966 stenotic effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 241000699670 Mus sp. Species 0.000 description 1
- 206010057469 Vascular stenosis Diseases 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000000747 cardiac effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 208000029078 coronary artery disease Diseases 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007639 printing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000004984 smart glass Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000002560 therapeutic procedure Methods 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
- 231100000216 vascular lesion Toxicity 0.000 description 1
Abstract
A method, apparatus, computing device, and storage medium for calculating fractional blood reserve FFR are provided. The method may include: acquiring vascular structure data; processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion; determining a hemodynamic model based on the coarse blood vessel portion and the fine blood vessel portion, the hemodynamic model including a first sub-model corresponding to the coarse blood vessel portion and a second sub-model corresponding to the fine blood vessel portion; FFR results are obtained based on the hemodynamic model.
Description
Technical Field
The present disclosure relates to the field of assisted diagnosis and intelligent medical, and in particular to a method, apparatus, computing device and storage medium for calculating fractional blood reserve FFR.
Background
The fractional flow reserve FFR refers to the ratio of the maximum blood flow that can be obtained in the region of the myocardium supplied by a coronary artery in the presence of a stenotic lesion to the maximum blood flow that can be obtained in the theoretically normal condition of the same region, and reflects the degree of arterial health, etc. A method that enables non-invasive FFR measurements is desired.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been recognized in any prior art unless otherwise indicated.
Disclosure of Invention
According to an aspect of the present disclosure, there is provided a method for calculating fractional blood reserve FFR, comprising: acquiring vascular structure data; processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion; determining a hemodynamic model based on the coarse blood vessel portion and the fine blood vessel portion, the hemodynamic model including a first sub-model corresponding to the coarse blood vessel portion and a second sub-model corresponding to the fine blood vessel portion; FFR results are obtained based on the hemodynamic model.
According to another aspect of the present disclosure, there is provided an apparatus for calculating fractional blood reserve FFR, comprising: a blood vessel data acquisition unit for acquiring blood vessel structure data; a blood vessel processing unit for processing the blood vessel structure data to obtain a coarse blood vessel portion and a fine blood vessel portion; a model establishing unit for determining a hemodynamic model based on the coarse blood vessel portion and the fine blood vessel portion, the hemodynamic model including a first sub-model corresponding to the coarse blood vessel portion and a second sub-model corresponding to the fine blood vessel portion; and a result calculation unit for obtaining an FFR result based on the hemodynamic model.
According to another aspect of the present disclosure, there is provided a computing device comprising: a memory, a processor, and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement a method for calculating a fractional blood reserve FFR in accordance with one or more embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements a method for calculating a fractional blood reserve FFR according to one or more embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a method for calculating a fractional blood reserve FFR according to one or more embodiments of the present disclosure.
These and other aspects of the disclosure will be apparent from and elucidated with reference to the embodiments described hereinafter.
Drawings
Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments, with reference to the following drawings, wherein:
FIG. 1 is a schematic diagram illustrating an example system in which various methods described herein may be implemented, according to an example embodiment;
FIG. 2 is a flowchart illustrating a method for calculating fractional blood reserve FFR according to an exemplary embodiment;
FIG. 3 is a schematic diagram illustrating a hemodynamic model in accordance with one or more embodiments of the present disclosure;
FIG. 4 is a schematic block diagram illustrating an apparatus for calculating fractional blood reserve FFR according to an exemplary embodiment;
fig. 5 is a block diagram illustrating an exemplary computer device that can be applied to exemplary embodiments.
Detailed Description
In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.
The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. As used herein, the term "plurality" means two or more, and the term "based on" should be interpreted as "based at least in part on". Furthermore, the term "and/or" and "at least one of … …" encompasses any and all possible combinations of the listed items.
Exemplary embodiments of the present disclosure are described in detail below with reference to the attached drawings.
FIG. 1 is a schematic diagram illustrating an example system 100 in which various methods described herein may be implemented, according to an example embodiment.
Referring to fig. 1, the system 100 includes a client device 110, a server 120, and a network 130 communicatively coupling the client device 110 with the server 120.
Client device 110 includes a display 114 and a client Application (APP) 112 that is displayable via display 114. The client application 112 may be an application program that needs to be downloaded and installed before running or an applet (liteapp) that is a lightweight application program. In the case where the client application 112 is an application program that needs to be downloaded and installed before running, the client application 112 may be pre-installed on the client device 110 and activated. In the case where the client application 112 is an applet, the user 102 may run the client application 112 directly on the client device 110 by searching the client application 112 in the host application (e.g., by name of the client application 112, etc.) or by scanning a graphical code (e.g., bar code, two-dimensional code, etc.) of the client application 112, etc., without installing the client application 112. In some embodiments, the client device 110 may be any type of mobile computer device, including a mobile computer, a mobile phone, a wearable computer device (e.g., a smart watch, a head-mounted device, including smart glasses, etc.), or other type of mobile device. In some embodiments, client device 110 may alternatively be a stationary computer device, such as a desktop, server computer, or other type of stationary computer device. In some alternative embodiments, the client device 110 may also be or include a medical image printing device.
Server 120 is typically a server deployed by an Internet Service Provider (ISP) or Internet Content Provider (ICP). Server 120 may represent a single server, a cluster of multiple servers, a distributed system, or a cloud server providing basic cloud services (such as cloud databases, cloud computing, cloud storage, cloud communication). It will be appreciated that although server 120 is shown in fig. 1 as communicating with only one client device 110, server 120 may provide background services for multiple client devices simultaneously.
Examples of network 130 include a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), and/or a combination of communication networks such as the internet. The network 130 may be a wired or wireless network. In some embodiments, the data exchanged over the network 130 is processed using techniques and/or formats including hypertext markup language (HTML), extensible markup language (XML), and the like. In addition, all or some of the links may also be encrypted using encryption techniques such as Secure Sockets Layer (SSL), transport Layer Security (TLS), virtual Private Network (VPN), internet protocol security (IPsec), and the like. In some embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above.
The system 100 may also include an image acquisition device 140. In some embodiments, the image acquisition device 140 shown in fig. 1 may be a medical scanning device including, but not limited to, scanning or imaging devices used in an positron emission tomography computer imaging system (Positron emission tomography, PET), an positron emission tomography computer imaging system (Positron emission tomography with computerized tomography, PET/CT), a single photon emission computed tomography computer imaging system (Single photon emission computed tomography with computerized tomography, SPECT/CT), a computed tomography system (Computerized tomography, CT), a medical ultrasound examination computer imaging system (Medical ultrasonography), a nuclear magnetic resonance imaging system (Nuclear magnetic resonance imaging, NMRI), a magnetic resonance imaging system (Magnetic Resonance Imaging, MRI), a angiographic imaging system (Cardiac angiography, CA), a digital radiography system (Digital radiography, DR), and the like. For example, the image acquisition device 140 may include a digital subtraction angiography scanner, a magnetic resonance angiography scanner, a tomographic scanner, an electron emission tomography scanner, an electron emission computed tomography scanner, a single photon emission computed tomography scanner, a medical ultrasound examination device, a nuclear magnetic resonance imaging scanner, a digital radiography scanner, or the like. The image acquisition device 140 may be connected to a server (e.g., server 120 in fig. 1 or a separate server of the imaging system, not shown in the figures) to enable processing of image data, including but not limited to converting scan data (e.g., into a medical image sequence), compression, pixel correction, three-dimensional reconstruction, and the like.
Image capture device 140 may be connected to client device 110, for example, via network 130, or otherwise directly connected to the client device to communicate with the client device.
Optionally, the system may also include a smart computing device or computing card 150. The image capture device 140 may include or be connected (e.g., removably connected) to such a computing card 150 or the like. As one example, the computing card 150 may implement processing of image data including, but not limited to, conversion, compression, pixel correction, reconstruction, and the like. As another example, computing card 150 may implement a method for calculating fractional blood reserve FFR according to an embodiment of the present disclosure.
The system may also include other parts not shown, such as a data store. The data store may be a database, data store or other form of one or more devices for data storage, may be a conventional database, and may include cloud databases, distributed databases, and the like. For example, direct image data formed by the image acquisition device 140 or a sequence of medical images or three-dimensional image data obtained by image processing, etc. may be stored in a data store for retrieval from the data store by the subsequent server 120 and client device 110. In addition, the image capturing device 140 may also directly provide the image data or the medical image sequence obtained through image processing or the three-dimensional image data to the server 120 or the client device 110.
A user may view the captured image or imagery, including preliminary image data or an image processed by analysis, etc., using the client device 110, view analysis results, such as coarse and fine vessel marking results and/or FFR calculation results as embodiments of the present disclosure, interact with the captured image or analysis results, input capture instructions, configuration data, and the like. The client device 110 may send configuration data, instructions, or other information to the image capture device 140 to control the capture and data processing of the image capture device, etc.
For purposes of embodiments of the present disclosure, in the example of fig. 1, the client application 112 may be an image sequence management application that may provide various functions, such as storage management, indexing, ordering, sorting, and the like, of the acquired image sequence. In response, the server 120 may be a server for use with an image sequence management application. The server 120 may provide image sequence management services to client applications 112 running in the client device 110, such as managing cloud image sequence storage, storing and categorizing image sequences by specified index (including, for example, but not limited to, sequence type, patient identification, body part, acquisition goal, acquisition stage, acquisition machine, whether there is lesion detection, severity, etc.), retrieving and providing image sequences to the client device by specified index, etc., based on user requests or instructions generated in accordance with embodiments of the present disclosure, etc. Alternatively, the server 120 may also provide or allocate such service capabilities or storage space to the client device 110, provide corresponding image sequence management services by the client application 112 running in the client device 110 according to user requests or instructions or the like generated according to embodiments of the present disclosure, and so forth. It is to be understood that the above gives only one example, and the present disclosure is not limited thereto.
Fig. 2 is a flowchart illustrating a method 200 for calculating fractional blood reserve FFR according to an exemplary embodiment. The method 200 may be performed at a client device (e.g., the client device 110 shown in fig. 1), i.e., the subject of execution of the steps of the method 200 may be the client device 110 shown in fig. 1. In some embodiments, the method 200 may be performed at a server (e.g., the server 120 shown in fig. 1). In some embodiments, the method 200 may be performed by a client device (e.g., the client device 110) and a server (e.g., the server 120) in combination.
Hereinafter, each step of the method 200 will be described in detail taking the execution subject as the client device 110 as an example.
At step 210, vascular structure data is acquired. The vascular structure data may be various types of data capable of characterizing at least one of vascular geometry, topology, length, connection, including but not limited to topological maps, vascular stereoscopic three-dimensional images, and the like. The vascular structure data may be for the human body to be analyzed.
At step 220, the vascular structure data is processed to obtain a coarse vascular portion and a fine vascular portion.
At step 230, a hemodynamic model is determined based on the coarse and fine blood vessel portions, the hemodynamic model including a first sub-model corresponding to the coarse blood vessel portion and a second sub-model corresponding to the fine blood vessel portion. Determining the hemodynamic model may include establishing the hemodynamic model such that the established hemodynamic model includes two sub-model portions corresponding to the coarse and fine vessel portions, respectively. Determining the hemodynamic model may also include modifying the established hemodynamic model, e.g., marking a portion of the established hemodynamic model as a first sub-model (or a second sub-model), and modifying parameters thereof; alternatively, all parameters of the hemodynamic model to be established may be modified. It is to be understood that the use of "models" and "submodels" herein is for illustrative purposes only, e.g., the step of determining a first submodel and a second submodel may also be referred to as establishing a hydrodynamic model based on the coarse and fine vessel portions, respectively, and the established hemodynamic model including the first and second submodels may also be referred to as a set of hydrodynamic models, a merged hydrodynamic model, or a set of hydrodynamic models, etc., and the disclosure is not limited thereto.
At step 240, FFR results for the human body to be analyzed are obtained based on the hemodynamic model.
By the above method, FFR can be calculated more accurately using the information of the thickness or width data of the blood vessel.
During the diagnostic process of certain diseases, an analysis of the health of blood vessels is required. For example, in diagnosing coronary heart disease, it is often necessary to look at the health of the coronary artery. At one angle, the analysis may be performed from a stenosis rate perspective. In another aspect, the determination may also be functionally based on Fractional Flow Reserve (FFR). FFR refers to the ratio of the maximum blood flow that can be obtained in the region of the myocardium supplied by the vessel to the maximum blood flow that can be obtained in the theoretically normal case of the same region in the case where there is a stenotic lesion in the coronary artery, i.e., the ratio of the average pressure (Pd) in the coronary artery at the far end of the stenosis in the state of maximum congestion of the myocardium to the average pressure (Pa) in the aortic artery at the mouth of the coronary artery. Furthermore, a method that enables non-invasive FFR measurements is desired.
In FFR analysis, one condition that may be encountered is chronic total occlusive lesions (chronictotal occlusion, CTO). In order to calculate and analyze CTO, it is often necessary to model the vessel topology, such as hydrodynamic models, tree-like models, etc. In the related art, there have been some methods of calculating whether a CTO exists based on a blood flow model, and it is understood that the present disclosure is not limited thereto.
According to some embodiments, processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion may include: the vascular structure data is divided into a coarse vascular portion and a fine vascular portion based on a predetermined vascular width threshold. For example, the vessel width threshold may be 1mm, 3mm, 4.2mm, etc., and the above is merely an example, and the present disclosure is not limited thereto.
According to some optional additional embodiments, the predetermined vessel width threshold may be a first vessel width threshold selected from a set of vessel width thresholds, the first vessel width threshold being selected according to a characteristic of a human body to which the vessel structure data corresponds. For example, the overall blood vessel thickness varies from person to person, so different blood vessel width thresholds may be selected for different persons. Alternatively, different vascular thresholds may be selected for different people based on other characteristics of the human body, such as blood viscosity, density, etc.
According to some embodiments, processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion may include: calculating reynolds numbers re=ρvd/μ at least two positions from the blood vessel structure data, where ρ represents a blood density, v represents a blood flow velocity, d represents a blood vessel diameter, and μ represents a viscosity coefficient of blood; and dividing the vascular structure data into a coarse vascular portion and a fine vascular portion based on the reynolds number. This can be achieved, for example, by a simple reynolds number comparison, or when using the Murray coefficient q=cd γ In the case of modeling blood vessels, this can be achieved by fitting a map of Reynolds numbers to Murray coefficients, as will be described further below.
According to some embodiments, processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion may include: the coarse and fine vessel portions are acquired by inputting the vessel structure data into a pre-trained model. Such a pre-trained model may be obtained by training on annotated data. For example, the sample of the training model may be a plurality of vascular structure data (e.g., a vascular topological graph, a three-dimensional image sequence, a two-dimensional image sequence, etc.), and thick and thin portions in the vascular structure data are labeled by a practitioner to obtain labeled "thick vascular portions" and "thin vascular portions", and the ability to label thick and thin blood vessels based on the vascular data may be obtained after learning such sample by the model.
According to some embodiments, acquiring vessel topology data may include: acquiring a sequence of images and obtaining the vessel topology data by performing a digital subtraction angiography process on the sequence of images. Digital subtraction angiography (Digital subtraction angiography, DSA) refers to that images of angiography are digitally processed to remove unwanted tissue images, and only blood vessel images are reserved, so that relatively clear and high-resolution images can be obtained through digital subtraction technology, thereby facilitating the observation of vascular lesions, positioning measurement of vascular stenosis and the like, and providing real stereoscopic images for diagnosis and interventional therapy. According to some embodiments, such an image sequence may be a CT image sequence. It is to be understood that the present disclosure is not so limited. For example, a DSA image may be acquired as an input, and a method according to an embodiment of the present disclosure is performed based on the DSA image. For another example, vessel geometry topology may be estimated based on CT taken images, then ranked according to importance, etc., to implement methods according to embodiments of the present disclosure. As another non-limiting example, DSA images may also be received as input and processed to obtain images in CT form, and the method according to embodiments of the present disclosure may be performed on the image sequence or images so obtained, and so forth. It is to be understood that the present disclosure is not limited in this regard and that other ways of acquiring or other forms of data may be used as vessel topology data.
According to some embodiments, determining a hemodynamic model based on the coarse and fine blood vessel portions may include: using the formula q=cd γ To model the blood vessel, where Q represents blood flow, d represents vessel width, C and γ are coefficients. For the coarse partA vascular portion, γ takes a first value, and for the fine vascular portion, γ takes a second value, the first value being greater than the second value.
From Morray's law, it is known that Q.alpha.d γ Where the index γ will take different values depending on the flow conditions, e.g. γ=3 for laminar flow where the boundary layer of the circular duct is fully developed and γ=2.33 for turbulent flow where the circular duct is fully developed. For other flow structures, γ should be in between. For a coarse vessel with a larger vessel diameter, the Reynolds number of the flow is relatively high, so that the flow state should be closer to turbulent flow; for small vessels with smaller vessel diameters, the reynolds number of the flow is relatively low, and the vessel wall has a greater influence on the flow, so that the flow state should be closer to laminar flow. So a smaller value should be taken for the coarse vessel γ; for fine blood vessels gamma a larger value should be taken. Thereby, the blood vessel model can be established more accurately.
According to some embodiments, it may also include according to formula R end ＝P end /Q end To calculate the resistance value of the vessel end.
Fig. 3 illustrates an example diagram of a hemodynamic model in accordance with one or more embodiments of the present disclosure. As shown in fig. 3, three vessel segments are shown, with flow rates Q, Q and Q2, respectively, and two corresponding vessel end resistances R1 and R2. It is understood that the FFR to be calculated may be P0/P2.
According to some embodiments, obtaining FFR results based on the hemodynamic model may include: and solving a model part corresponding to the crude blood vessel part in the blood fluid mechanical model. For example, the model portion of the hemodynamic model corresponding to the thin vessel portion may not be solved, which in such embodiments corresponds to performing the CTO algorithm on only the thick vessel, thereby greatly reducing computational effort and still maintaining relatively satisfactory accuracy. Alternatively, only a model portion of the hemodynamic model corresponding to the thin blood vessel portion is partially solved. For example, the blood vessel is labeled first as a rough blood vessel or a fine blood vessel, and then analysis of the rupture of the rough blood vessel, optimization of the boundary, and the like are performed. For fine blood vessels, the break therein may be ignored. Firstly, dividing blood vessels, carrying out different processing paths on different groups, and carrying out different processing during solving, so that the calculated amount can be greatly reduced, and a better effect can be still obtained.
According to some embodiments, processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion may include: a coarse vessel portion in the form of a three-dimensional image and a fine vessel portion in the form of a two-dimensional image are obtained. Different vessel portions may be represented in different data forms, e.g. three-dimensional results may be obtained for coarse vessels to preserve more information, while only length and width values may be preserved for fine vessel portions.
According to one or more embodiments of the present disclosure, by marking the vessel width, performing different processes on the modeling and/or solution model, the vessel width information can be better utilized, resulting in more accurate results. In addition, the appropriate simplification of the thin blood vessel portion can reduce the amount of calculation and increase the processing efficiency.
Although the operations are depicted in the drawings in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in sequential order, nor should it be understood that all illustrated operations must be performed in order to achieve desirable results.
It will be appreciated that throughout this disclosure, the image sequence may be or may include two-dimensional image data, as well as three-dimensional image data. The image sequence may be image data that is directly acquired and stored or otherwise transmitted to the terminal device for use by the user. The image sequence may also be processed image data after various image processing. The image sequence may also be subjected to other analysis processes (e.g., an analysis process of whether a lesion feature or lesion is present) and contain analysis results (e.g., the circling of a region of interest, the segmentation results of tissue, etc.). It is to be understood that the present disclosure is not so limited.
Fig. 4 is a schematic block diagram illustrating an apparatus 400 for calculating fractional blood reserve FFR according to an exemplary embodiment. The apparatus 400 for calculating the fractional blood reserve FFR may include: a blood vessel data acquisition unit 410, a blood vessel processing unit 420, a model building unit 430, and a result calculation unit 440. The blood vessel data acquisition unit 410 may be used to acquire blood vessel structure data. The vascular processing unit 420 may be used to process the vascular structure data to obtain a coarse vascular portion and a fine vascular portion. The model building unit 430 may be configured to determine a hemodynamic model based on the coarse blood vessel portion and the fine blood vessel portion, the hemodynamic model comprising a first sub-model corresponding to the coarse blood vessel portion and a second sub-model corresponding to the fine blood vessel portion. The result calculation unit 440 may be configured to obtain FFR results based on the hemodynamic model.
It should be appreciated that the various modules of the apparatus 400 shown in fig. 4 may correspond to the various steps in the method 200 described with reference to fig. 2. Thus, the operations, features, and advantages described above with respect to method 200 apply equally to apparatus 400 and the modules that it comprises. For brevity, certain operations, features and advantages are not described in detail herein.
According to an embodiment of the present disclosure, a computing device is also disclosed, comprising a memory, a processor and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement the steps of the method for calculating fractional blood reserve FFR and variants thereof according to embodiments of the present disclosure.
According to an embodiment of the present disclosure, there is also disclosed a non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for calculating a fractional blood reserve FFR according to an embodiment of the present disclosure, and variants thereof.
According to an embodiment of the present disclosure, a computer program product is also disclosed, comprising a computer program, wherein the computer program when executed by a processor realizes the steps of the method for calculating fractional blood reserve FFR and variants thereof according to embodiments of the present disclosure.
Although specific functions are discussed above with reference to specific modules, it should be noted that the functions of the various modules discussed herein may be divided into multiple modules and/or at least some of the functions of the multiple modules may be combined into a single module. The particular module performing the actions discussed herein includes the particular module itself performing the actions, or alternatively the particular module invoking or otherwise accessing another component or module that performs the actions (or performs the actions in conjunction with the particular module). Thus, a particular module that performs an action may include that particular module itself that performs the action and/or another module that the particular module invokes or otherwise accesses that performs the action. As used herein, the phrase "entity a initiates action B" may refer to entity a issuing an instruction to perform action B, but entity a itself does not necessarily perform that action B.
It should also be appreciated that various techniques may be described herein in the general context of software hardware elements or program modules. The various modules described above with respect to fig. 4 may be implemented in hardware or in hardware in combination with software and/or firmware. For example, the modules may be implemented as computer program code/instructions configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules may be implemented as hardware logic/circuitry. The SoC may include an integrated circuit chip including one or more components of a processor (e.g., a central processing unit (Central Processing Unit, CPU), microcontroller, microprocessor, digital signal processor (Digital Signal Processor, DSP), etc.), memory, one or more communication interfaces, and/or other circuitry, and may optionally execute received program code and/or include embedded firmware to perform functions.
According to an aspect of the present disclosure, a computing device is provided that includes a memory, a processor, and a computer program stored on the memory. The processor is configured to execute a computer program to implement the steps of any of the method embodiments described above.
According to an aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the method embodiments described above.
According to an aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the method embodiments described above.
Illustrative examples of such computer devices, non-transitory computer readable storage media, and computer program products are described below in connection with fig. 5.
Fig. 5 illustrates an example configuration of a computer device 500 that may be used to implement the methods described herein. For example, the server 120 and/or client device 110 shown in fig. 1 may include an architecture similar to that of the computer device 500. The above-described devices/means for calculating fractional blood reserve FFR may also be implemented, in whole or at least in part, by computer device 500 or a similar device or system.
The computer device 500 may be a variety of different types of devices, such as a server of a service provider, a device associated with a client (e.g., a client device), a system-on-chip, and/or any other suitable computer device or computing system. Examples of computer device 500 include, but are not limited to: a desktop, server, notebook, or netbook computer, a mobile device (e.g., tablet, cellular, or other wireless telephone (e.g., smart phone), notepad computer, mobile station), a wearable device (e.g., glasses, watch), an entertainment appliance (e.g., an entertainment appliance, a set-top box communicatively coupled to a display device, a gaming machine), a television or other display device, an automotive computer, and so forth. Thus, computer device 500 may range from full resource devices (e.g., personal computers, game consoles) that have significant memory and processor resources, to low-resource devices with limited memory and/or processing resources (e.g., traditional set-top boxes, hand-held game consoles).
Computer device 500 may include at least one processor 502, memory 504, communication interface(s) 506, display device 508, other input/output (I/O) devices 510, and one or more mass storage devices 512, capable of communicating with each other, such as through a system bus 514 or other suitable connection.
The processor 502 may be a single processing unit or multiple processing units, all of which may include a single or multiple computing units or multiple cores. The processor 502 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. The processor 502 may be configured to, among other capabilities, obtain and execute computer-readable instructions stored in the memory 504, mass storage device 512, or other computer-readable medium, such as program code for the operating system 516, program code for the application programs 518, program code for other programs 520, and so forth.
Memory 504 and mass storage device 512 are examples of computer-readable storage media for storing instructions that are executed by processor 502 to implement the various functions as previously described. For example, memory 504 may generally include both volatile memory and nonvolatile memory (e.g., RAM, ROM, etc.). In addition, mass storage device 512 may generally include hard disk drives, solid state drives, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CD, DVD), storage arrays, network attached storage, storage area networks, and the like. Memory 504 and mass storage device 512 may both be referred to herein collectively as memory or a computer-readable storage medium, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that may be executed by processor 502 as a particular machine configured to implement the operations and functions described in the examples herein.
A number of program modules may be stored on the mass storage device 512. These programs include an operating system 516, one or more application programs 518, other programs 520, and program data 522, and they may be loaded into the memory 504 for execution. Examples of such application programs or program modules may include, for example, computer program logic (e.g., computer program code or instructions) for implementing the following components/functions.
Although illustrated in fig. 5 as being stored in memory 504 of computer device 500, modules 516, 518, 520, and 522, or portions thereof, may be implemented using any form of computer readable media accessible by computer device 500. As used herein, "computer-readable medium" includes at least two types of computer-readable media, namely computer storage media and communication media.
Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information for access by a computer device.
In contrast, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism. Computer storage media as defined herein do not include communication media.
The computer device 500 may also include one or more communication interfaces 506 for exchanging data with other devices, such as via a network, direct connection, or the like, as previously discussed. Such communication interfaces may be one or more of the following: any type of network interface (e.g., a Network Interface Card (NIC)), a wired or wireless (such as IEEE 802.11 Wireless LAN (WLAN)) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, bluetooth, etc TM An interface, a Near Field Communication (NFC) interface, etc. Communication interface 506 mayCommunication is facilitated within a variety of networks and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the internet, and so forth. Communication interface 506 may also provide for communication with external storage devices (not shown) such as in a storage array, network attached storage, storage area network, or the like.
In some examples, a display device 508, such as a monitor, may be included for displaying information and images to a user. Other I/O devices 510 may be devices that receive various inputs from a user and provide various outputs to the user, and may include touch input devices, gesture input devices, cameras, keyboards, remote controls, mice, printers, audio input/output devices, and so on.
While the disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative and schematic and not restrictive; the present disclosure is not limited to the disclosed embodiments. Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed subject matter, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps than those listed and the word "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
Claims (14)
1. A method for calculating fractional blood reserve FFR, comprising:
acquiring vascular structure data;
processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion;
determining a hemodynamic model based on the coarse blood vessel portion and the fine blood vessel portion, the hemodynamic model including a first sub-model corresponding to the coarse blood vessel portion and a second sub-model corresponding to the fine blood vessel portion; and
FFR results are obtained based on the hemodynamic model.
2. The method of claim 1, wherein processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion comprises:
the vascular structure data is divided into a coarse vascular portion and a fine vascular portion based on a predetermined vascular width threshold.
3. The method of claim 2, wherein the predetermined vessel width threshold is a first vessel width threshold selected from a set of vessel width thresholds, the first vessel width threshold selected based on characteristics of a human body to which the vessel structure data corresponds.
4. The method of claim 1, wherein processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion comprises:
calculating reynolds numbers at least two locations from the vascular structure data; and
the vessel structure data is divided into a coarse vessel portion and a fine vessel portion based on reynolds numbers at the at least two locations.
5. The method of claim 1, wherein processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion comprises:
the coarse and fine vessel portions are acquired by inputting the vessel structure data into a pre-trained model.
6. The method of any of claims 1-5, wherein acquiring vessel topology data comprises:
acquiring a sequence of images, and
the vessel topology data is obtained by performing a digital subtraction angiography process on the image sequence.
7. The method of claim 6, wherein the image sequence is a CT image sequence.
8. The method of any of claims 1-5, wherein determining a hemodynamic model based on the coarse and fine blood vessel portions comprises:
based on the formula q=cd γ To determine the hemodynamic model, wherein Q represents blood flow, d represents vessel width, C and γ are coefficients, and wherein for the first submodel γ takes a first value and for the second submodel γ takes a second value, the first value being greater than the second value.
9. The method of any of claims 1-5, wherein obtaining FFR results based on the hemodynamic model comprises:
and solving a model part corresponding to the crude blood vessel part in the blood fluid mechanical model.
10. The method of any of claims 1-5, wherein processing the vascular structure data to obtain a coarse vascular portion and a fine vascular portion comprises: a coarse vessel portion in the form of a three-dimensional image and a fine vessel portion in the form of a two-dimensional image are obtained.
11. An apparatus for calculating fractional blood reserve FFR, comprising:
a blood vessel data acquisition unit for acquiring blood vessel structure data;
a blood vessel processing unit for processing the blood vessel structure data to obtain a coarse blood vessel portion and a fine blood vessel portion;
a model establishing unit for determining a hemodynamic model based on the coarse blood vessel portion and the fine blood vessel portion, the hemodynamic model including a first sub-model corresponding to the coarse blood vessel portion and a second sub-model corresponding to the fine blood vessel portion; and
and a result calculation unit for obtaining FFR results based on the hemodynamic model.
12. A computing device, comprising:
a memory, a processor and a computer program stored on the memory,
wherein the processor is configured to execute the computer program to implement the steps of the method of any one of claims 1-10.
13. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method of any of claims 1-10.
14. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the method of any of claims 1-10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210220910.5A CN116779135A (en) | 2022-03-08 | 2022-03-08 | Method, apparatus, computing device and medium for calculating fractional blood reserve |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210220910.5A CN116779135A (en) | 2022-03-08 | 2022-03-08 | Method, apparatus, computing device and medium for calculating fractional blood reserve |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116779135A true CN116779135A (en) | 2023-09-19 |
Family
ID=87986472
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210220910.5A Pending CN116779135A (en) | 2022-03-08 | 2022-03-08 | Method, apparatus, computing device and medium for calculating fractional blood reserve |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116779135A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117476241A (en) * | 2023-12-28 | 2024-01-30 | 柏意慧心(杭州)网络科技有限公司 | Method, computing device and medium for determining a blood flow of a blood vessel |
-
2022
- 2022-03-08 CN CN202210220910.5A patent/CN116779135A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117476241A (en) * | 2023-12-28 | 2024-01-30 | 柏意慧心(杭州)网络科技有限公司 | Method, computing device and medium for determining a blood flow of a blood vessel |
| CN117476241B (en) * | 2023-12-28 | 2024-04-19 | 柏意慧心(杭州)网络科技有限公司 | Method, computing device and medium for determining a blood flow of a blood vessel |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11622812B2 (en) | Systems and methods for correction of artificial deformation in anatomic modeling | |
| EP2800517B1 (en) | Method and system for sensitivity analysis in modeling blood flow characteristics | |
| JP5584006B2 (en) | Projection image generation apparatus, projection image generation program, and projection image generation method | |
| US10262101B2 (en) | Systems and methods for predicting perfusion deficits from physiological, anatomical, and patient characteristics | |
| EP3359041B1 (en) | Mobile ffr simulation | |
| WO2018133098A1 (en) | Vascular wall stress-strain state acquisition method and system | |
| Ghobrial et al. | The new role of diagnostic angiography in coronary physiological assessment | |
| JP2020171687A (en) | Systems and methods for processing 3d anatomical volumes based on localization of 2d slices thereof | |
| CN116779135A (en) | Method, apparatus, computing device and medium for calculating fractional blood reserve | |
| JPWO2018159708A1 (en) | Apparatus and method for blood flow analysis and program | |
| CN114048738A (en) | Data acquisition method, device, computing equipment and medium based on symptom description | |
| CN117116485A (en) | Data processing method, device, computing equipment and storage medium | |
| JP7195458B2 (en) | Fluid analysis device, method and program | |
| JP7368500B2 (en) | Fluid analysis equipment, methods and programs | |
| CN117912699A (en) | Blood flow field information determining method, model obtaining method, device and computing equipment | |
| CN117153344A (en) | Data processing method, device, computing equipment and storage medium | |
| KR20230049589A (en) | Method, device, computing device and storage medium for determining blood flow rate | |
| CN117541742A (en) | Image processing method, device, computing equipment and storage medium | |
| Ogiela et al. | Cognitive methods for semantic image analysis in medical imaging applications |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: Rooms 303, 304, 305, 321 and 322, building 3, No. 11, Chuangxin Road, science and Technology Park, Changping District, Beijing Applicant after: Shukun Technology Co.,Ltd. Address before: Rooms 303, 304, 305, 321 and 322, building 3, No. 11, Chuangxin Road, science and Technology Park, Changping District, Beijing Applicant before: Shukun (Beijing) Network Technology Co.,Ltd. |
|
| CB02 | Change of applicant information |