WO2025044005A1 - Data processing method and apparatus, computing device, and storage medium - Google Patents

Abstract

A data processing method and apparatus, a computing device, and a storage medium. The data processing method comprises: acquiring image data related to a target human body area; processing the image on the basis of at least one constraint condition, to determine at least one parameter related to fractional flow reserve (FFR) of the target human body area, wherein the at least one constraint condition comprises at least one fluid-mechanics model; and determining a predicted FFR result of the target human body area on the basis of the at least one parameter.

Description

Data processing method, device, computing equipment and storage medium

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese patent application 2023111266436 filed on September 1, 2023, the entire contents of which are incorporated by reference in their entirety into this application.

Technical Field

The present disclosure relates to the field of data processing, and in particular to a data processing method, apparatus, computing device and storage medium.

Background Art

The blood flow reserve fraction FFR refers to the ratio of the maximum blood flow that can be obtained in the myocardial area supplied by the blood vessel to the maximum blood flow that can be obtained in the same area under normal conditions in theory when there is a stenotic lesion in the coronary artery. It can reflect the health of the artery, etc. A method that can obtain the predicted value of FFR in a non-invasive way is desired.

The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art.

Summary of the invention

According to one aspect of the present disclosure, there is provided a data processing method, comprising: acquiring image data related to a target human body region; and processing the image based on at least one constraint condition to determine at least one parameter related to a blood reserve fraction FFR of the target human body region, wherein the at least one constraint condition comprises at least one fluid mechanics model; and obtaining a predicted FFR result of the target human body region based on the at least one parameter.

According to another aspect of the present disclosure, there is provided a data processing device, comprising: an image acquisition unit, for acquiring image data related to a target human body region; an image processing unit, for processing the image based on at least one constraint condition to determine at least one parameter related to a blood reserve fraction FFR of the target human body region, wherein the at least one constraint condition includes at least one fluid mechanics model; and a result determination unit, for obtaining a predicted FFR result of the target human body region based on the at least one parameter.

According to another aspect of the present disclosure, a computing device is provided, including: 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 data processing method according to one or more embodiments of the present disclosure.

According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the data processing method according to one or more embodiments of the present disclosure is implemented.

According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements the data processing method 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.

BRIEF DESCRIPTION OF THE DRAWINGS

Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

FIG1 is a schematic diagram illustrating an example system in which various methods described herein may be implemented according to an exemplary embodiment;

FIG2 is a flow chart illustrating a data processing method according to an exemplary embodiment;

FIG3 illustrates an example of image data to which a method according to an exemplary embodiment can be applied;

FIG4 is a schematic block diagram illustrating a data processing apparatus according to an exemplary embodiment;

FIG. 5 is a block diagram illustrating an exemplary computer device that can be applied to the exemplary embodiments.

DETAILED DESCRIPTION

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

The terms used in the description of various examples described in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. 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". In addition, the terms "and/or" and "at least one of..." cover any one of the listed items and all possible combinations.

Exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

FIG. 1 is a schematic diagram illustrating an example system 100 in which the various methods described herein may be implemented, according to an example embodiment.

1 , the system 100 includes a client device 110 , a server 120 , and a network 130 that communicatively couples the client device 110 and the server 120 .

The client device 110 includes a display 114 and a client application (APP) 112 that can be displayed via the display 114. The client application 112 can be an application that needs to be downloaded and installed before running or a small program (liteapp) as a lightweight application. In the case where the client application 112 is an application that needs to be downloaded and installed before running, the client application 112 can be pre-installed on the client device 110 and activated. In the case where the client application 112 is a small program, the user 102 can directly run the client application 112 on the client device 110 by searching for the client application 112 in the host application (for example, by the name of the client application 112, etc.) or scanning the graphic code of the client application 112 (for example, a bar code, a QR code, etc.), without installing the client application 112. In some embodiments, the client device 110 can be any type of mobile computer device, including a mobile computer, a mobile phone, a wearable computer device (for example, a smart watch, a head-mounted device, including smart glasses, etc.) or other types of mobile devices. In some embodiments, the client device 110 may alternatively be a stationary computer device, such as a desktop computer, a server computer or other types of stationary computer devices. In some optional embodiments, the client device 110 may also be or may include a medical image printing device.

The server 120 is typically a server deployed by an Internet Service Provider (ISP) or an Internet Content Provider (ICP). The server 120 may represent a single server, a cluster of multiple servers, a distributed system, or a cloud server that provides basic cloud services (such as cloud databases, cloud computing, cloud storage, and cloud communications). It will be understood that although FIG. 1 shows that the server 120 communicates with only one client device 110, the server 120 may provide background services for multiple client devices at the same time.

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. Network 130 can be a wired or wireless network. In some embodiments, the data exchanged through network 130 is processed using technologies and/or formats including hypertext markup language (HTML), extensible markup language (XML), etc. In addition, encryption technologies such as secure socket layer (SSL), transport layer security (TLS), virtual private network (VPN), Internet protocol security (IPsec) can also be used to encrypt all or some links. In some embodiments, customized and/or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

The system 100 may further 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 a positron emission tomography computer imaging system (PET), a positron emission tomography computer imaging system (PET/CT), a single photon emission tomography system, or a medical imaging system. Scanning or imaging devices used in computerized tomography (SPECT/CT), computerized tomography (CT), medical ultrasonography, nuclear magnetic resonance imaging (NMRI), magnetic resonance imaging (MRI), cardiovascular angiography (CA), digital radiography (DR), etc. For example, the image acquisition device 140 may include a digital subtraction angiography scanner, a magnetic resonance angiography scanner, a tomographic angiography scanner, a positron emission tomography scanner, a positron emission computed tomography scanner, a single photon emission computed tomography scanner, a computed tomography scanner, a medical ultrasonography device, a nuclear magnetic resonance imaging scanner, a magnetic resonance imaging scanner, a digital radiography scanner, etc. The image acquisition device 140 can be connected to a server (e.g., the server 120 in FIG. 1 or a separate server of the imaging system not shown in the figure) to implement image data processing, including but not limited to converting the scanned data (e.g., converting it into a medical image sequence), compressing it, correcting its pixels, and reconstructing it in three dimensions.

The image acquisition device 140 may be connected to the client device 110 , for example, via the network 130 , or directly connected to the client device in other ways to communicate with the client device.

Optionally, the system may further include an intelligent computing device or computing card 150. The image acquisition device 140 may include or be connected (e.g., removably connected) to such a computing card 150, etc. As an example, the computing card 150 may implement image data processing, including but not limited to conversion, compression, pixel correction, reconstruction, etc. As another example, the computing card 150 may implement a data processing method according to an embodiment of the present disclosure.

The system may also include other parts not shown, such as a data storage unit. The data storage unit may be a database, a data repository, or one or more devices for data storage in other forms, and may be a conventional database, or may include a cloud database, a distributed database, etc. For example, direct image data formed by the image acquisition device 140 or a medical image sequence or three-dimensional image data obtained through image processing may be stored in the data storage unit for subsequent retrieval by the server 120 and the client device 110 from the data storage unit. In addition, the above-mentioned image acquisition device 140 may also directly provide the image data or the medical image sequence or three-dimensional image data obtained through image processing to the server 120 or the client device 110, etc.

The user can use the client device 110 to control the acquisition of images or videos, view the acquired images or videos (including preliminary image data or images that have been analyzed and processed, etc.), view the analysis results, interact with the acquired images or analysis results, input acquisition instructions, configure data, etc. The client device 110 can send configuration data, instructions or other information to the image acquisition device 140 to control the acquisition of the image acquisition device, process data, etc.

For the purpose of the embodiments of the present disclosure, in the example of FIG. 1 , the client application 112 may be an image sequence management application, which may provide various functions, such as storage management, indexing, sorting, and classification of the acquired image sequences. Accordingly, the server 120 may be a server used together with the image sequence management application. The server 120 may provide image sequence management services to the client application 112 running in the client device 110 based on user requests or instructions generated according to the embodiments of the present disclosure, such as managing the image sequence storage in the cloud, storing and classifying the image sequence according to a specified index (including, for example, but not limited to, sequence type, patient identification, body part, acquisition target, acquisition stage, acquisition machine, whether lesions are detected, severity, etc.), and retrieving and providing the image sequence to the client device according to the specified index, etc. Alternatively, the server 120 may also provide or allocate such service capabilities or storage space to the client device 110, and the client application 112 running in the client device 110 may provide corresponding image sequence management services according to user requests or instructions generated according to the embodiments of the present disclosure, etc. It can be understood that the above is only one example, and the present disclosure is not limited thereto.

FIG2 is a flow chart illustrating a data processing method 200 according to an exemplary embodiment. The method 200 may be performed at a client device (e.g., the client device 110 shown in FIG1 ), that is, the execution subject of each step of the method 200 may be the client device 110 shown in FIG1 . In some embodiments, the method 200 may be performed at a server (e.g., the server 120 shown in FIG1 ). 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.

In the following, each step of the method 200 is described in detail by taking the execution subject as the client device 110 as an example.

2 , at step 210 , image data related to a target human body region is acquired.

At step 220, the image is processed based on at least one constraint to determine at least one parameter related to the fractional blood reserve (FFR) of the target body region, wherein the at least one constraint comprises at least one fluid dynamics model.

At step 230 , a predicted FFR result of the target human body region is obtained based on the at least one parameter.

Through the above method, fluid mechanics constraints can be imposed in the FFR-related calculation process, so that various output parameters meet the constraints of fluid mechanics, which is more in line with the actual situation and is therefore more accurate.

During the diagnosis process of certain diseases, it is necessary to analyze the health of blood vessels. For example, when diagnosing coronary heart disease, it is often necessary to look at the health of the coronary arteries. From one perspective, it can be analyzed from the perspective of stenosis rate. On the other hand, it can also be judged from the functional aspect based on the blood flow reserve fraction (FFR). FFR refers to the ratio of the maximum blood flow that can be obtained by the myocardial area supplied by the blood vessel in the presence of coronary artery stenosis to the maximum blood flow that can be obtained by the same area under normal conditions in theory, that is, the average blood flow in the distal coronary artery of the stenosis under the state of maximum myocardial congestion. The ratio of the coronary artery pressure (Pd) to the mean aortic pressure (Pa) at the coronary artery ostia. In addition, a method capable of measuring FFR noninvasively is desired.

According to various embodiments of the present disclosure, the output parameters may be a direct FFR result mask image, or may be various blood parameters used to calculate FFR, such as blood pressure p, blood flow Q, blood flow velocity v, etc.

It should be understood that due to individual differences, without professional analysis and confirmation by a doctor, the user's disease diagnosis conclusion or health status cannot be directly drawn based solely on the predicted value obtained by method 200. Method 200 is essentially a data processing method, which processes image data and other data through imposed constraints. The entire process does not require the participation of a doctor, and the obtained predicted value can only provide a reference for doctors, medical workers, etc. Therefore, such a method does not prevent doctors from freely choosing diagnosis and treatment plans and should not be regarded as a disease diagnosis method.

According to some embodiments, processing the image based on at least one constraint condition to determine at least one parameter related to the blood reserve fraction FFR of the target human body region may include: using a pre-trained neural network to determine the FFR result related to the target human body region, wherein the at least one fluid mechanics model is used as a loss function of the neural network.

It is understandable that in such an embodiment, various types and structures of neural network models may be used. As a specific non-limiting example, a U-net segmentation network may be used, but the present disclosure is not limited thereto.

When training a neural network to extract relevant parameters, common loss functions such as Dice loss function, CE loss, etc. can be used. However, after such loss converges, results that do not conform to natural laws may be obtained, such as blood pressure increasing after passing through a section of artery, etc. Therefore, it is expected to be constrained by fluid mechanics formulas.

In some embodiments, in addition to using the form of a loss function, fluid mechanics constraints can also be applied in other forms. In some examples, the results can be checked once after each iteration to see if they meet the constraints of fluid mechanics, and adjustments can be made if they do not meet the requirements. In some examples, in addition to the loss function on the training side, multiple such iterations can be performed when the model is actually applied, for example, to ensure that the adjacent pixels (or part of the pixels) actually output by the model meet the constraints including the fluid mechanics constraints, so as to be more in line with the actual situation.

According to some embodiments, the at least one fluid mechanics model may include a fluid mechanics conservation equation. As a non-limiting example, the fluid mechanics conservation equation may be a Navier-Stokes equation, or other fluid mechanics related constraint equations.

According to some embodiments, the fluid mechanics conservation equation may include a momentum conservation equation. According to some other additional or alternative embodiments, the fluid mechanics conservation equation may include a mass conservation equation.

According to some embodiments, wherein the at least one constraint may also include at least one restriction. In such an embodiment, in addition to the fluid mechanics constraint, other restrictions may also be adopted (e.g., a restriction is applied once for each iteration). According to some embodiments, the at least one restriction may include at least one of the following: a boundary condition, an initial condition, a true value of a measurement point. In other embodiments, other restrictions may also be included, such as restrictions on a specific area, a specific lesion or plaque shape, or specific patient characteristics, etc.

According to some embodiments, the at least one parameter may include a fluid mechanics parameter for one or more acquisition points in the image. In some examples, one or more acquisition points in the image may be constrained at the fluid mechanics level to ensure that the points themselves and/or the points are in accordance with the corresponding natural laws of fluid mechanics.

The term "collection point" used here may refer to a pixel point or an area composed of several or more pixel points. For example, the fluid mechanics parameters of adjacent pixels or adjacent areas may be required to comply with the fluid mechanics constraints (and optionally, the constraints of other restrictive conditions). For another example, pixels or areas that are not adjacent but have a specific relationship (e.g., have a specific positional relationship, are adjacent to the same third area, etc.) may be required to comply with the fluid mechanics constraints (and optionally, the constraints of other restrictive conditions). In other examples, the fluid mechanics constraints may be for a single pixel or area.

According to some embodiments, the one or more acquisition points may be key points in the image.

For example, key points may include points in a region of interest, such as points in a region of interest determined in advance or by other algorithms. For another example, key points may be key points determined based on geometric shapes, such as key points at specific human body positions, blood vessel positions, plaque positions, or organs or tissues of specific shapes, etc. In such a case, fluid mechanics constraints may be sparsely applied to the pixels in the image, thereby saving computing resources, while still achieving accurate results that conform to the facts.

According to some embodiments, the fluid mechanics parameter may include at least one of the following: volume force, pressure, pressure change. It is understood that the present disclosure is not limited thereto and may be applicable to other blood parameters, as long as the corresponding parameter itself or its deformation can be substituted into the corresponding fluid mechanics rule and can be directly or indirectly used for the calculation of FFR.

A specific non-limiting embodiment is described below. In such a specific example, the loss function of the physical constraint may include two parts:

L＝ω _pde *L _pde +ω _data *L _data

Among them, L _pde and L _data are the loss functions corresponding to the “control equation” and “data” constraints respectively, and ω _pde and ω _data are weight coefficients.

In such an embodiment, the role of _Lpde may be to ensure that the output of the neural network satisfies the physical governing equations. It will be appreciated that the above are merely examples.

For example, the Navier-Stokes equations can be used to describe the physical process of blood flow.

In such an embodiment, _Lpde can be written as follows:

Where _Npde is the number of sampling points in the computational domain, ^xi and ^ti are spatial position coordinates and time, respectively. and They are the blood flow rate and pressure output by the neural network respectively. is the blood volume force output by the neural network, ρ and v are the density and viscosity coefficient of blood respectively.

It is understandable that, in such an example, f ₁ ( ^xi , ^ti ) = 0 may correspond to momentum conservation, and f ₂ ( ^xi , ^ti ) = 0 may correspond to mass conservation. In such a specific example, the prediction result of the network may include the prediction condition U and the body force P, but it is understandable that the definition of U and P may not be limited to body force, constraint condition, etc.

Furthermore, it is to be understood that the present disclosure is not limited thereto. Other neural network outputs can also be expressed as long as they can be substituted into the conservation equation. For example, the original formula can be written as:

Where f ₀ (x ⁱ , t ⁱ ) is the body force. It can be understood that in the embodiment described above, the output of the neural network is

As another example, in the network output In the case of , the momentum conservation formula can be updated as follows:

Returning to the example above, the role of L _data can be to ensure that the output of the neural network meets the given constraints. L _data can include constraints such as boundary conditions, initial conditions, and true values of measurement points, or other constraints set according to actual conditions (for example, set by the user or automatically determined by the system).

For example, L _data can be written as follows:

Where N _data is the number of sampling points where the constraint is applied, u ⁱ (x ⁱ ,t ⁱ ) is the given constraint, and can be the corresponding neural network output.

According to such an embodiment, two conservation conditions are constructed as constraints of fluid mechanics, and it is understood that the present disclosure is not limited thereto. For example, more or fewer conservation conditions may be used as constraints. In such a case, the output of the neural network may be used to construct equations for the conservation conditions. In other examples, if the output of the neural network includes other terms, such as It is also possible to include other terms in momentum conservation instead of body force or in addition to body force. As an example, the output of the neural network can be body force, body force + delta P, etc. According to one or more embodiments, the output of the network and fluid mechanics parameters can be used to construct constraints through fluid mechanics conservation equations.

According to one or more embodiments of the present disclosure, the image data related to the target human body region may be a single image or an image sequence, such as a time series or a three-dimensional space sequence, and the like. The image data related to the target human body region may be image data containing the target human body region, such as originally acquired image data. The image data related to the target human body region may be processed image data. As a specific non-limiting example, the image data related to the target human body region may include multiple images or multiple image sequences, for example, one image (or image sequence) is a processed region segmentation map (e.g., a region segmentation mask), and another image (or image sequence) is a corresponding acquisition value (e.g., a CT value mask map), and the like.

Although the various operations are depicted in the drawings as being in a particular order, this should not be understood as requiring that the operations must be performed in the particular order shown or in a sequential order, nor should it be understood as requiring that all of the operations shown must be performed to obtain the desired result. For example, two steps described in order herein may be performed in reverse order, or may be performed concurrently. For another example, one or more steps in the various embodiments of the present disclosure may be omitted.

It is to be understood that throughout the present disclosure, an image sequence may be or may include two-dimensional image data, or may be or include three-dimensional image data. An image sequence may be image data that is directly acquired and stored or otherwise sent to a terminal device for use by a user. An image sequence may also be processed image data after various image processing. An image sequence may also undergo other analysis processes (e.g., an analysis process for whether there are lesion features or lesions) and include analysis results (e.g., circling of a region of interest, results of tissue segmentation, etc.). It is to be understood that the present disclosure is not limited thereto. As shown in FIG. 3 , an example 300 of a CT angiography image is shown, in which a blood vessel 311 is provided. And plate 312, various parameters can be predicted based on such an image, and the FFR result can be calculated based on the predicted parameters such as P0 and P0', and the present disclosure is not limited to this.

It will be understood that throughout this disclosure, terms such as "fluid mechanics model" may be or may include fluid mechanics formulas, functions, curves, lookup tables, etc., and may be or may include mathematical models, network models, units, etc. established based on such formulas, functions, curves, lookup tables, and the present disclosure is not limited thereto.

4 is a schematic block diagram illustrating a data processing device 400 according to an exemplary embodiment. The data processing device 400 may include an image acquisition unit 410, an image processing unit 420, and a result determination unit 430. The image acquisition unit 410 may be used to acquire image data related to a target human body region. The image processing unit 420 may be used to process the image based on at least one constraint condition to determine at least one parameter related to the blood reserve fraction FFR of the target human body region, wherein the at least one constraint condition includes at least one fluid mechanics model. The result determination unit 430 may be used to obtain a predicted FFR result of the target human body region based on the at least one parameter.

It should be understood that the various modules of the device 400 shown in FIG4 may correspond to the various steps in the method 200 described with reference to FIG2. Thus, the operations, features and advantages described above for the method 200 are also applicable to the device 400 and the modules included therein. For the sake of brevity, some operations, features and advantages are not described in detail herein.

According to an embodiment of the present disclosure, a computing device is also disclosed, including 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 data processing method according to the embodiment of the present disclosure and its variant examples.

According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium is also disclosed, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the data processing method according to the embodiment of the present disclosure and its variant examples are implemented.

According to an embodiment of the present disclosure, a computer program product is also disclosed, including a computer program, wherein when the computer program is executed by a processor, the steps of the data processing method according to the embodiment of the present disclosure and its variant examples are implemented.

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 multiple modules may be combined into a single module. The specific module discussed herein performing an action includes the specific module itself performing the action, or alternatively the specific module calling or otherwise accessing another component or module that performs the action (or performs the action in conjunction with the specific module). Therefore, the specific module performing an action may include the specific module itself that performs the action and/or another module that the specific module calls or otherwise accesses to perform the action. For example, the various modules or units described in accordance with one or more embodiments of the present disclosure may be combined into a single module or unit in some embodiments. For another example, two or more modules or units may be described in parallel in one or more embodiments of the present disclosure, while in some other embodiments, two or more modules or units may be described in parallel. In the example, there may be one or more inclusion relationships between these modules and units. As used herein, the phrase "entity A initiates action B" or "entity A causes action B to be performed" may mean that entity A issues an instruction to perform action B, but entity A itself does not necessarily perform the action B. For example, the phrase "display module module causes display..." may mean that the display module instructs a display (not shown) or other possible display device to display, and the display module itself does not need to perform the action of "displaying".

It should also be understood that various technologies 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, these modules may be implemented as computer program codes/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/circuits. For example, in some embodiments, one or more of the modules or units described in accordance with one or more embodiments of the present disclosure may be implemented together in a system on chip (SoC). SoC may include an integrated circuit chip (which includes a processor (e.g., a central processing unit (CPU), a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and/or one or more components in other circuits), and may optionally execute the received program code and/or include embedded firmware to perform functions.

According to one aspect of the present disclosure, a computing device is provided, which includes a memory, a processor, and a computer program stored in the memory. The processor is configured to execute the computer program to implement the steps of any method embodiment described above.

According to one aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any method embodiment described above are implemented.

According to one aspect of the present disclosure, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the steps of any method embodiment described above are implemented.

Illustrative examples of such a computer device, a non-transitory computer-readable storage medium, and a computer program product are described below in conjunction with FIG. 5 .

FIG5 shows an example configuration of a computer device 500 that can be used to implement the methods described herein. For example, the server 120 and/or the client device 110 shown in FIG1 may include an architecture similar to the computer device 500. The above-mentioned data processing device/apparatus may also be implemented in whole or in part by the computer device 500 or a similar device or system.

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 a chip, and/or any other suitable computer device or Computing system. Examples of computer device 500 include, but are not limited to: a desktop computer, a server computer, a laptop or netbook computer, a mobile device (e.g., a tablet computer, a cellular or other wireless phone (e.g., a smart phone), a notepad computer, a mobile station), a wearable device (e.g., glasses, a watch), an entertainment device (e.g., an entertainment appliance, a set-top box communicatively coupled to a display device, a game console), a television or other display device, a car computer, etc. Thus, computer device 500 can range from a full-resource device with large amounts of memory and processor resources (e.g., a personal computer, a game console) to a low-resource device with limited memory and/or processing resources (e.g., a traditional set-top box, a handheld game console).

Computer device 500 may include at least one processor 502, memory 504, communication interface(s) 506, a display device 508, other input/output (I/O) devices 510, and one or more mass storage devices 512, all capable of communicating with one another, such as via a system bus 514 or other appropriate connections.

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 circuits, and/or any device that manipulates signals based on operating instructions. Among other capabilities, the processor 502 may be configured to obtain and execute computer-readable instructions stored in the memory 504, mass storage device 512, or other computer-readable media, such as program code of an operating system 516, program code of an application program 518, program code of other programs 520, and the like.

The memory 504 and the mass storage device 512 are examples of computer-readable storage media for storing instructions that are executed by the processor 502 to implement the various functions described above. For example, the memory 504 may generally include both volatile memory and non-volatile memory (e.g., RAM, ROM, etc.). In addition, the mass storage device 512 may generally include a hard drive, a solid-state drive, a removable medium, including external and removable drives, a memory card, a flash memory, a floppy disk, an optical disk (e.g., a CD, a DVD), a storage array, a network attached storage, a storage area network, etc. The memory 504 and the mass storage device 512 may all be collectively referred to herein as memory or computer-readable storage media, and may be a non-transitory medium capable of storing computer-readable, processor-executable program instructions as computer program code, which may be executed by the processor 502 as a specific machine configured to implement the operations and functions described in the examples herein.

A number of program modules may be stored on mass storage device 512. These programs include operating system 516, one or more application programs 518, other programs 520, and program data 522, and they may be loaded into memory 504 for execution. Examples of such applications or program modules may include, for example, computer program logic (e.g., computer program code or instructions) for implementing components/functionality including method 200 (including any suitable steps of method 200) and/or other embodiments described herein.

5 as being stored in the memory 504 of the computer device 500, the modules 516, 518, 520, and 522, or portions thereof, may be implemented using any form of computer-readable media accessible by the computer device 500. As used herein, "computer-readable media" 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 by 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 disk (DVD), or other optical storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other non-transmission media that 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 does 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 through a network, direct connection, etc., as discussed above. 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 an 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, a Bluetooth ^™ interface, a Near Field Communication (NFC) interface, etc. The communication interface 506 may facilitate communication within a variety of network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. The communication interface 506 may also provide communication with an external storage device (not shown) such as a storage array, a network attached storage, a storage area network, etc.

In some examples, a display device 508 such as a monitor may be included for displaying information and images to the user. Other I/O devices 510 may be devices that receive various inputs from the 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 the like.

Although the present disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description should be considered illustrative and exemplary rather than 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 by studying the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not Other elements or steps are excluded 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)

A data processing method, comprising: Acquiring image data related to a target human body region; and Processing the image based on at least one constraint to determine at least one parameter related to the fractional blood reserve (FFR) of the target body region, wherein the at least one constraint comprises at least one fluid dynamics model; and A predicted FFR result of the target human body region is obtained based on the at least one parameter. The method of claim 1, wherein processing the image based on at least one constraint condition to determine at least one parameter related to the blood reserve fraction FFR of the target body region comprises: An FFR result associated with the target body region is determined using a pre-trained neural network, wherein the at least one fluid dynamics model is used as a loss function of the neural network. The method according to claim 1 or 2, wherein the at least one fluid dynamics model includes fluid dynamics conservation equations. The method of claim 3, wherein the fluid dynamics conservation equation comprises a momentum conservation equation. The method according to claim 3 or 4, wherein the fluid dynamics conservation equation includes a mass conservation equation. The method according to any one of claims 1 to 5, wherein the at least one constraint condition further comprises at least one restriction condition. The method according to claim 6, wherein the at least one constraint condition comprises at least one of the following: a boundary condition, an initial condition, and a true value of a measurement point. The method according to any one of claims 1-7, wherein the at least one parameter comprises a fluid dynamics parameter for one or more acquisition points in the image. The method of claim 8, wherein the one or more acquisition points are key points in the image. The method according to claim 8 or 9, wherein the fluid mechanics parameter includes at least one of the following: volume force, pressure, and pressure change. A data processing device, comprising: An image acquisition unit, used to acquire image data related to a target human body region; an image processing unit, configured to process the image based on at least one constraint condition to determine at least one parameter related to a blood reserve fraction FFR of the target human body region, wherein the at least one constraint condition comprises at least one fluid mechanics model; and A result determination unit is used to obtain a predicted FFR result of the target human body region based on the at least one parameter. A computing device comprising: a memory, a processor and a computer program stored on the memory, The processor is configured to execute the computer program to implement the steps of the method according to any one of claims 1 to 10. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 10. A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.