Method and device for determining fractional flow reserve, computing equipment and storage medium

Abstract

The invention provides a method and device for determining fractional flow reserve, computing equipment and a storage medium. The method comprises the following steps: acquiring medical image data of a target blood vessel; identifying at least one region of interest capable of causing a pressure change based on the medical image data; determining at least one local pressure change indicator associated with the identified at least one region of interest, respectively; and determining a fractional flow reserve (FFR) value at at least one target location in the target blood vessel based on the at least one local pressure change indicator.

Description

Technical Field

[0001] This disclosure relates to the field of data processing, and in particular to a method, apparatus, computing device, and storage medium for determining fractional blood flow reserve. Background Technology

[0002] Fractional flow reserve (FFR) refers to the ratio of the maximum blood flow available to the myocardial region supplied by a coronary artery in the presence of stenosis to the theoretically maximum blood flow available to the same region under normal conditions. It reflects the health of the artery. A non-invasive method for measuring FFR is desired.

[0003] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0004] According to one aspect of this disclosure, a method for determining the fractional flow reserve is provided, comprising: acquiring medical imaging data of a target blood vessel; identifying at least one region of interest capable of causing pressure changes based on the medical imaging data; determining at least one local pressure change index associated with the identified at least one region of interest; and determining a fractional flow reserve (FFR) value at at least one target location in the target blood vessel based on the at least one local pressure change index.

[0005] According to another aspect of this disclosure, an apparatus for determining the fractional flow reserve (FFR) is provided, comprising: a medical image data acquisition unit for acquiring medical image data of a target blood vessel; a region of interest (ROI) identification unit for identifying at least one ROI capable of causing pressure changes based on the medical image data; a local pressure change index determination unit for determining at least one local pressure change index associated with the identified at least one ROI; and a FFR determination unit for determining a fractional flow reserve (FFR) value at at least one target location in the target blood vessel based on the at least one local pressure change index.

[0006] According to another aspect of this disclosure, a computing device is provided, 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 determining a fraction of blood flow reserve according to one or more embodiments of this disclosure.

[0007] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for determining a fraction of blood flow reserve according to one or more embodiments of this disclosure.

[0008] According to another aspect of this disclosure, a computer program product is provided, including a computer program, wherein the computer program, when executed by a processor, implements a method for determining a fractional blood flow reserve according to one or more embodiments of this disclosure.

[0009] These and other aspects of this disclosure will be apparent from the embodiments described below, and will be elucidated with reference to the embodiments described below. Attached Figure Description

[0010] Further details, features, and advantages of this disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a schematic diagram illustrating an example system in which various methods described herein may be implemented according to exemplary embodiments; Figure 2 This is a flowchart illustrating a method for determining fractional blood flow reserve according to an exemplary embodiment; Figure 3 This is a schematic block diagram illustrating an apparatus for determining a fraction of blood flow reserve according to an exemplary embodiment; Figure 4 This is a block diagram illustrating an exemplary computer device that can be applied to an exemplary embodiment. Detailed Implementation

[0011] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0012] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. As used herein, the term "multiple" means two or more, and the term "based on" should be interpreted as "at least partially based on". Furthermore, the terms "and / or" and "at least one of..." cover any one of the listed items and all possible combinations thereof.

[0013] Exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0014] Figure 1 This is a schematic diagram illustrating an example system 100 in which various methods described herein may be implemented according to exemplary embodiments.

[0015] refer to Figure 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.

[0016] Client device 110 includes a display 114 and a client application (APP) 112 that can be displayed on the display 114. Client application 112 can be an application that needs to be downloaded and installed before running, or a lightweight application (liteapp). If client application 112 is an application that needs to be downloaded and installed before running, client application 112 can be pre-installed on client device 110 and activated. If client application 112 is a mini-app, user 102 can run client application 112 directly on client device 110 without installing it, by searching for client application 112 in the host application (e.g., by the name of client application 112) or by scanning the graphic code of client application 112 (e.g., barcode, QR code, etc.). In some embodiments, client device 110 can be any type of mobile computing device, including mobile computers, mobile phones, wearable computing devices (e.g., smartwatches, head-mounted devices 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, server computer, or other type of stationary computer device. In some alternative embodiments, the client device 110 may also be or may include a medical image printing device.

[0017] Server 120 is typically a server deployed by an Internet Service Provider (ISP) or Internet Content Provider (ICP). Server 120 can 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, and cloud communications. It will be understood that, although... Figure 1 The diagram shows that server 120 communicates with only one client device 110, but server 120 can provide background services to multiple client devices simultaneously.

[0018] Examples of network 130 include combinations of local area networks (LANs), wide area networks (WANs), personal area networks (PANs), and / or communication networks such as the Internet. Network 130 can be wired or wireless. In some embodiments, technologies and / or formats including Hypertext Markup Language (HTML), Extensible Markup Language (XML), etc., are used to process data exchanged through network 130. Furthermore, encryption technologies such as Secure Sockets Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), and Internet Protocol Security (IPsec) can be used to encrypt all or some of the links. In some embodiments, custom and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0019] System 100 may further include image acquisition device 140. In some embodiments, Figure 1 The image acquisition device 140 shown may be a medical scanning device, including but not limited to scanning or imaging devices used in positron emission tomography (PET), positron emission tomography with computerized tomography (PET / CT), single photon emission computed tomography with computerized tomography (SPECT / CT), computed tomography (CT), medical ultrasonography, nuclear magnetic resonance imaging (NMRI), magnetic resonance imaging (MRI), cardiovascular angiography (CA), digital radiography (DR), etc. For example, image acquisition device 140 may include digital subtraction angiography scanner, magnetic resonance angiography scanner, computed tomography angiography scanner, positron emission tomography scanner, positron emission tomography (PET) scanner, single photon emission computed tomography (SPT) scanner, computed tomography scanner, medical ultrasound examination equipment, magnetic resonance imaging (MRI) scanner, digital radiography scanner, etc. Image acquisition device 140 may be connected to a server (e.g., Figure 1The system connects to server 120 (or a separate server of the imaging system, not shown in the figure) to process image data, including but not limited to converting scan data (e.g., converting it into a medical image sequence), compressing it, correcting pixels, and reconstructing it in three dimensions.

[0020] Image acquisition device 140 may be connected to client device 110, for example, via network 130, or otherwise directly connected to client device to communicate with client device.

[0021] Optionally, the system may also include an intelligent computing device or a computing card 150. The image acquisition device 140 may include or be connected (e.g., detachably connected) to such a computing card 150. As an example, the computing card 150 can perform image data processing, including but not limited to conversion, compression, pixel correction, reconstruction, etc. As another example, the computing card 150 can implement a method for determining the fractional blood flow reserve according to embodiments of this disclosure.

[0022] The system may also include other components not shown, such as a data storage unit. The data storage unit may be a database, data repository, or other form of device for data storage; it may be a conventional database, or it may include a cloud database, a distributed database, etc. For example, direct image data generated by the image acquisition device 140, or medical image sequences 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 client device 110. Furthermore, the image acquisition device 140 may also directly provide direct image data or medical image sequences or three-dimensional image data obtained through image processing to the server 120 or client device 110, etc.

[0023] Users can use client device 110 to control the acquisition of images or videos, view the acquired images or videos (including preliminary image data or images after analysis and processing), view analysis results, interact with the acquired images or analysis results, input acquisition commands, configure data, etc. Client device 110 can send configuration data, commands, or other information to image acquisition device 140 to control image acquisition device acquisition, data processing, etc.

[0024] For the purposes of this disclosure's embodiments, Figure 1In the example, client application 112 can be an image sequence management application that provides various functions, such as storage management, indexing, sorting, and classification of acquired image sequences. Correspondingly, server 120 can be a server used in conjunction with the image sequence management application. Server 120 can provide image sequence management services to client application 112 running on client device 110 based on user requests or instructions generated according to embodiments of this disclosure. For example, it can manage image sequence storage in the cloud, store and classify image sequences according to specified indexes (including, but not limited to, sequence type, patient identifier, body part, acquisition target, acquisition stage, acquisition machine, presence of lesions, severity, etc.), and retrieve and provide image sequences to client devices according to specified indexes, etc. Alternatively, server 120 can also provide or allocate such service capabilities or storage space to client device 110, whereby client application 112 running on client device 110 provides corresponding image sequence management services based on user requests or instructions generated according to embodiments of this disclosure, etc. It is understood that the above is only one example, and this disclosure is not limited thereto.

[0025] Figure 2 This is a flowchart illustrating a method 200 for determining a fractional blood flow reserve according to an exemplary embodiment. Method 200 can be implemented on a client device (e.g., Figure 1 The execution is performed at the client device 110 shown, that is, the execution entity of each step of method 200 can be... Figure 1 The client device 110 shown. In some embodiments, method 200 can be performed on a server (e.g., Figure 1 The method 200 is executed at server 120 (as shown in the figure). In some embodiments, the method 200 may be executed in combination by a client device (e.g., client device 110) and a server (e.g., server 120).

[0026] The steps of method 200 are described in detail below.

[0027] refer to Figure 2 In step 210, medical imaging data of the target blood vessel is acquired.

[0028] At step 220, based on the medical imaging data, at least one region of interest that can cause pressure changes is identified.

[0029] At step 230, at least one local pressure change index is determined that is associated with at least one region of interest identified.

[0030] At step 240, the fractional flow reserve (FFR) value at at least one target location in the target vessel is determined based on the at least one local pressure change index.

[0031] The above method allows for a more accurate calculation of the FFR value.

[0032] According to embodiments of this disclosure, the complex, global FFR calculation problem can be deconstructed into a problem of independently quantifying pressure changes in several local regions of interest (ROIs). Embodiments of this disclosure allow the model to focus its attention on key areas (such as lesions or specific morphologies) that have the greatest impact on hemodynamics, simplifying the model's learning task, improving computational efficiency and prediction accuracy, and also making the model's output (pressure drop values ​​in each locality) more clinically interpretable.

[0033] According to some embodiments, the region of interest may include at least one of a lesion region, a stenotic region, and a bifurcation region.

[0034] Such exemplary embodiments provide specific types of ROIs that play a decisive role in pressure changes, thereby improving computational efficiency and prediction accuracy.

[0035] On the other hand, by focusing the model training on sources of blood flow resistance such as lesions, stenosis, and bifurcation, it can be ensured that the model learns morpho-pressure relationships with real physical meaning, thereby avoiding interference from irrelevant noise or artifacts in the images and further improving the stability of FFR prediction.

[0036] In one embodiment, the lesion area can be further subdivided into calcified plaque areas and non-calcified (soft) plaque areas. Exemplarily, stenotic areas can be classified according to their degree of stenosis (e.g., mild, moderate, severe). Exemplarily, bifurcation areas can be further subdivided according to the angle of bifurcation and the ratio of the diameter of the main branch to the branch at the bifurcation. Furthermore, the region of interest may also include other known hemodynamically relevant areas, such as tortuous segments of the vessel (e.g., >90 degrees) or areas where endovascular stents are implanted, and this disclosure is not limited thereto.

[0037] According to some embodiments, determining at least one local pressure change index associated with at least one identified region of interest includes determining the at least one local pressure change index by a prediction model, wherein the type of the at least one region of interest is input into the prediction model.

[0038] According to this embodiment, a differentiated and more refined prediction model is achieved. By inputting the type of ROI as additional information into the prediction model, such as as a prompt, the model can invoke different internal parameters or processing logic for different types of ROIs. For example, the model can learn that bifurcation mainly causes local turbulent pressure drop, while "narrowing" mainly causes frictional pressure drop. This differentiated processing capability makes the calculation of each local pressure drop more accurate.

[0039] In some additional or alternative implementations, the type of region of interest (ROI) can be used as a one-hot encoded vector, concatenated with the image data, and input into the prediction model. In another implementation, a dedicated subnetwork can be trained for each ROI type. When determining local pressure change indicators, the system dynamically selects and calls the corresponding subnetwork for calculation based on the input ROI type.

[0040] According to some embodiments, determining the fractional flow reserve (FFR) value at at least one target location in the target vessel based on the at least one local pressure change index may include obtaining the FFR value at the target location based on the pressure change index preceding the target location among the at least one local pressure change index.

[0041] For example, the FFR value at the target location can be obtained by summing the local pressure change indices preceding the target location from the at least one local pressure change index. According to this embodiment, an FFR calculation path based on a physical series model is provided. This method treats the entire blood vessel as a series of cascaded local resistance elements (i.e., ROIs), and the final FFR value can be quickly and intuitively obtained by simply summing the pressure drops of all ROIs upstream of the target location. This method is fast, has clear physical meaning, and is very suitable for clinical scenarios requiring rapid assessment of the cumulative effects of multiple lesions.

[0042] In some additional or alternative implementations, the pressure change index can be a weighted sum of pressure change indices; for example, the pressure drop value of the ROI closer to the aortic orifice has a lower weight. In another implementation, if the index is a proportional change in FFR, the FFR value can be obtained by multiplication, for example... .

[0043] According to some embodiments, the method may further include determining a background pressure change index, and wherein determining a fractional flow reserve (FFR) value at at least one target location in the target vessel based on the at least one local pressure change index includes determining the FFR value based on the at least one local pressure change index and the background pressure change index. It is understood that the background pressure change index may also be a change in background pressure drop, a change in background FFR, etc., and this disclosure is not limited thereto.

[0044] This embodiment provides a more complete FFR calculation model. In this embodiment, the decrease in FFR is considered to consist of two parts: a slow decrease caused by friction and gradual morphological changes in healthy blood vessels (i.e., background pressure change index), and a sharp decrease caused by local lesions or other regions of interest (i.e., local pressure change index). By separating and then superimposing these two components, the model can more accurately simulate the real hemodynamic process, avoiding the neglect of background pressure drop when calculating local indicators separately, thereby improving the overall prediction accuracy.

[0045] In some additional or alternative implementations, the background pressure change index can be determined by a separate background model trained on vascular images with all identified ROIs removed or masked. In another implementation, the master prediction model can have two parallel output heads, one outputting the local pressure change index and the other outputting the background pressure change index, which are summed or otherwise combined in a final stage to obtain the final FFR value.

[0046] According to some embodiments, determining at least one local pressure change index associated with at least one identified region of interest includes determining the at least one local pressure change index based on medical imaging data of the local region corresponding to the at least one region of interest.

[0047] This implementation enhances the model's ability to learn local features. By inputting only the local region image data corresponding to the ROI into the model, instead of the complete, massive whole-vascular tree image, the amount of data that needs to be processed is greatly reduced. This allows the model to be designed to be more lightweight, while also forcing the model to learn more deeply the direct relationship between local morphology and pressure drop, thus improving the model's robustness.

[0048] In some additional or alternative embodiments, the medical imaging data of a local region can be a three-dimensional voxel cube centered at the centroid of the ROI. In another embodiment, it can be a three-dimensional model of a vascular segment extending a certain length within and around the ROI along the vessel's centerline. In yet another embodiment, it can be a stack of multiple representative two-dimensional (2D) cross-sectional images within the ROI region. Exemplarily, at least one local pressure drop value can be determined based on a subset of local image data corresponding to the at least one region of interest. Also exemplaryly, at least one local pressure drop value can be determined based on local three-dimensional volume data containing the at least one region of interest.

[0049] According to some embodiments, the size of the local region is determined based on the diameter of the vessel containing the corresponding region of interest. This embodiment can further address the scale dependence problem of lesion morphology in vessels of different diameters. For example, a 50% stenosis in a proximal vessel with a diameter of 4 mm and a distal vessel with a diameter of 2 mm will have drastically different absolute sizes and hemodynamic effects. By dynamically determining the size of the local region based on the vessel diameter (e.g., always truncating twice the lesion length), the model can focus more on the local region rather than irrelevant regions, thus obtaining more accurate calculation results. Furthermore, the model can learn a scale-invariant law regarding the relative degree of stenosis, thereby enhancing the model's generalization ability.

[0050] In some additional or alternative implementations, the size of the local region can be determined as N times the reference diameter of the vessel at the location of the corresponding ROI (e.g., N=3). In another implementation, all captured local regions, regardless of their physical size, can be rescaled to a uniform standard size through interpolation or downsampling. Exemplarily, the original reference diameter of the vessel can also be input into the model as an additional numerical feature.

[0051] According to some embodiments, the medical imaging data includes the topology of the target blood vessel, which is modeled using a graph convolutional network. Exemplarily, the medical imaging data can take the form of a graph model. In such embodiments, the method may include, for example, constructing a graph model (e.g., as part of the medical imaging data) and identifying corresponding regions of interest (ROIs) on this graph. The corresponding ROIs may be specific nodes. Subsequently, local pressure change indices can be determined for these identified ROIs. Exemplarily, these local indices can be assigned as node attributes to the graph model, and then a graph neural network can be used to perform the final step of determining the fractional flow reserve (FFR). Exemplarily, constructing a graph model representing the topology of the target blood vessel may include multiple nodes and edges connecting the nodes, and the node positions are represented by the number of adjacent nodes. Exemplarily, the method may include inputting the graph model and the node attributes into a graph neural network, and processing the node attributes and the topology of the graph model through the graph neural network to determine the fractional flow reserve (FFR) value corresponding to at least one of the multiple nodes. In such embodiments, the identified ROIs capable of causing pressure changes can be associated with the graph model, for example, as nodes, as node attributes, etc.

[0052] According to this embodiment, the topological information of blood vessels can be incorporated into the calculation of FFR. The connectivity of blood vessels (such as whether a lesion is located in a main artery or a terminating branch) has a significant impact on blood flow distribution and distal pressure. By modeling this topology using methods such as Graph Convolutional Networks (GCNs), the model can obtain global contextual information beyond individual ROIs, understanding the "positional importance" of each ROI within the entire vascular tree, thereby more accurately assessing its contribution to FFR. For example, constructing a graph model representing the target vascular topology can include multiple nodes and edges connecting the nodes, with the node's position represented by the number of adjacent nodes.

[0053] As a non-limiting embodiment, in addition to local pressure change indicators, node attributes can also be used to describe one or more of the radiographic features, physical features, or topological features corresponding to the location of the corresponding sampling point.

[0054] According to some embodiments, imaging features may include vascular morphology. Exemplarily, vascular morphology may include at least one of the following: vessel thickness, gradation, curvature, and bifurcation angle. Exemplarily, vascular morphology may be measured or calculated directly from segmented 3D vascular images. According to some embodiments, imaging features may include lesion status, which may include the presence of a lesion, the length of the lesion, etc. According to some embodiments, imaging features may include image density features. It is understood that factors influencing density features may include statistical values ​​of contrast agent from bright to dark or grayscale values ​​of image pixels / voxels. Such embodiments introduce underlying features based on the grayscale values ​​of the original image, such as contrast agent statistics, thereby providing the model with information beyond simple morphological segmentation. For example, the filling of contrast agent may indirectly reflect blood flow velocity or pressure gradient, while certain density values ​​(such as high density) are directly related to calcification. This provides the model with richer, multimodal judgment criteria. According to some embodiments, imaging features may include plaque composition features. Understandably, plaque composition characteristics can be distinguished, for example, by CT values, such as differentiating between calcified and soft plaques. Such embodiments further refine lesion status and image density, taking into account the impact of plaques with different compositions (e.g., hard calcified plaques versus soft plaques) on blood flow. By inputting this feature into the graphical model, the model can learn this difference, thereby improving the physiological accuracy of the predictions.

[0055] According to some embodiments, topological features may include vessel location information. Topological features may include location logic information, thereby enabling the model to better understand the vessel topology and providing explicit contextual location information for each node in the graph model. Exemplarily, location information may include information about whether a node is distal, mid-, or proximal. By inputting this vessel location information as a topological feature, the model can learn this location dependency; for example, proximal stenosis may affect all its downstream branches, while distal stenosis may only affect its own distal end. It is understood that vessel location information can be a classification label, for example, classifying a node as proximal (e.g., 0-20 mm), mid-, or distal (e.g., >40 mm) based on its distance from the centerline of the coronary artery ostium; these values ​​are, as examples. Alternatively, it can be a normalized numerical value (e.g., between 0 and 1) representing its relative position within the total length of the current vessel segment. It is understood that this disclosure is not limited thereto.

[0056] According to some embodiments, topological features may include whether a node is a bifurcation point or a vascular terminal. For example, key topological points (bifurcation points and terminal points) can be explicitly defined as node attributes, thereby further reducing the learning difficulty of the model and enabling the model to identify key locations where hemodynamic changes occur drastically (such as blood flow separation and pressure loss) and invoke specialized processing logic, thereby improving the prediction accuracy for complex situations such as bifurcation lesions.

[0057] According to some embodiments, the medical imaging data includes multi-phase CT or CTA images of different cardiac cycles. Generally, to ensure the consistency of medical images, the processed data is often limited to single-phase images. However, according to embodiments of this disclosure, due to the model's generalization ability and focus on local regions of interest, the model can filter out interference from irrelevant information, thus gaining the ability to understand multi-phase images. In this case, by introducing temporal information, the model can capture the dynamic characteristics of blood vessels and lesions. For example, comparing systolic and diastolic images can reveal the elasticity of the vessel wall and the flexibility of plaques, both important factors affecting blood flow resistance. By effectively utilizing multi-phase data, the model can obtain richer and more physiologically accurate hemodynamic information than a single static image, thereby improving the accuracy of FFR prediction.

[0058] In some additional or alternative implementations, the two sets of image data from the systolic and diastolic phases can be input into the prediction model as two independent channels. In another implementation, a difference map or deformation field between the two phases can be calculated first, and this feature map representing "motion" can be used as additional input. In yet another implementation, a 4D (3D spatial + 1D temporal) convolutional kernel can be used to extract spatiotemporal features simultaneously.

[0059] According to some embodiments, the method may further include: straightening the vessel segment containing the region of interest before determining the at least one local pressure change index. Straightening the vessel segment standardizes the geometry. The natural curvature of a vessel introduces additional pressure drop and diversifies the morphology of the lesion on imaging. By "straightening" the vessel segment before computation, the effects of this curvature can be eliminated or normalized, allowing the model to focus more on learning the relationship between the morphological features of the lesion or bifurcation itself (such as stenosis cross-sectional area and length) and the resulting pressure drop, thereby simplifying the learning task and improving the model's accuracy.

[0060] In some additional or alternative implementations, the "straightening process" can be applied to the entire vessel segment containing the ROI. In another implementation, instead of physical straightening, the local curvature of the vessel segment can be calculated and input as an additional numerical feature into the prediction model, allowing the model to learn the effect of curvature. In yet another implementation, data augmentation techniques can be used during model training to randomly bend and straighten the vessel image, making the model inherently invariant to bending.

[0061] According to some embodiments, the pressure change index can be the pressure drop value or the change in FFR. It is understood that the local pressure change index can refer to the local pressure drop value or the change in local FFR (delta FFR). For example, FFR(P d / P a The value can be a dimensionless ratio, while the "pressure drop value" (Delta P = P) a - P d The quantity can be an absolute physical quantity with units (such as mmHg), and the two can be converted. In other words, according to various exemplary embodiments, the model can output the dimensionless change in FFR or the pressure drop value.

[0062] In some alternative embodiments, the "local pressure change index" can be a scalar value or a vector describing the pressure change characteristics of the ROI under different blood flow conditions. In some additional or alternative embodiments, the "local pressure change index" can also be a "local resistance coefficient" describing the degree of obstruction of blood flow by the ROI, and the final FFR can be calculated using hydrodynamic formulas. In another embodiment, the index can be an "equivalent diameter" or "functional stenosis percentage," where the model predicts the degree of ideal stenosis that the ROI is hemodynamically equivalent to, and then the pressure drop is calculated using this equivalent value; however, this disclosure is not limited to these methods.

[0063] Optionally, medical imaging data may include vessel segmentation data and lesion segmentation data. It is understood that, in some alternative embodiments, "medical imaging data" may be image data from coronary CTA, MRA, or intravascular ultrasound (IVUS). In some alternative embodiments, the identification step may be performed manually by a medical expert or automatically by a pre-trained image segmentation model (e.g., U-Net). In some alternative embodiments, the step of determining the FFR value may output an FFR distribution curve along the vessel centerline, or only output a single FFR value at the distal end of the vessel, and this disclosure is not limited thereto.

[0064] According to some embodiments, the method may further include receiving both vessel segmentation data and lesion segmentation data as input, and determining fusion features based on these two. This ensures that the model, when making predictions, must simultaneously consider both the global morphology of the vessel and the local pathological state, ensuring that lesion information crucial for FFR prediction is not ignored at the input stage. Exemplarily, determining the fusion features based on the vessel segmentation data and the lesion segmentation data using a feature extraction network may include extracting a first feature based on the vessel segmentation data and a second feature based on the lesion segmentation data using the feature extraction network; and fusing the first feature and the second feature to obtain the fusion features. For example, in addition to identifying the region of interest, the method may further include acquiring vessel segmentation data and lesion segmentation data of a target vessel, determining the fusion features based on the vessel segmentation data and the lesion segmentation data using a feature extraction network, and determining the fractional flow reserve (FFR) value at at least one target location in the target vessel using a prediction network. As a specific non-limiting example, in such an embodiment, the identified region of interest may be associated with one or both of the vessel segmentation data and the lesion segmentation data.

[0065] Although the various operations are depicted in the accompanying drawings in a specific order, this should not be construed as requiring that these operations must be performed in the specific order shown or in chronological order, nor should it be construed as requiring that all 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. As another example, one or more steps in the various embodiments of this disclosure may be omitted.

[0066] Furthermore, it is understood that the methods for predicting or determining data according to one or more embodiments of this disclosure are not methods for doctors to directly determine diagnostic results, but rather involve data processing or information processing processes during the medical process. The data processing results can be used for doctors' reference, thereby assisting doctors in their medical operations. It is understood that the information processing methods, data prediction methods, determination methods, decision-making methods, etc., according to one or more embodiments of this disclosure are executed by a computer or a device containing a computer.

[0067] It is understood that throughout this disclosure, images, image sequences, or images may be or may include two-dimensional image data, or may be or may include three-dimensional image data. Images, image sequences, or images may be image data that is directly acquired and stored or otherwise transmitted to a terminal device for user use. Images, image sequences, or images may also be processed image data after various image processing steps. Images, image sequences, or images may undergo other analytical processes (e.g., analysis of the presence of lesion features or lesions) and include analytical results (e.g., delineation of regions of interest, tissue segmentation results, etc.). It is understood that this disclosure is not limited thereto.

[0068] Figure 3 This is a schematic block diagram illustrating an apparatus 300 for determining the fractional flow reserve (FFR) according to an exemplary embodiment. The apparatus 300 for determining the FFR may include a medical image data acquisition unit 310, a region of interest (ROI) identification unit 320, a local pressure change index determination unit 330, and a FFR determination unit 340. The medical image data acquisition unit 310 is used to acquire medical image data of a target blood vessel. The ROI identification unit 320 is used to identify at least one ROI capable of causing pressure changes based on the medical image data. The local pressure change index determination unit 330 is used to determine at least one local pressure change index associated with each of the identified ROIs. The FFR determination unit 340 is used to determine the FFR value at at least one target location within the target blood vessel based on the at least one local pressure change index.

[0069] It should be understood that Figure 3 The various modules of the device 300 shown can be connected to the reference. Figure 2 The steps in method 200 described correspond to each other. Therefore, the operations, features, and advantages described above for method 200 and its variations also apply to apparatus 300 and its included modules. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0070] According to embodiments 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 method for determining a fraction of blood flow reserve according to embodiments of the present disclosure and variations thereof.

[0071] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium is also disclosed, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method for determining the fractional blood flow reserve and variations thereof according to embodiments of the present disclosure.

[0072] According to embodiments of the present disclosure, a computer program product is also disclosed, comprising a computer program, wherein when executed by a processor, the computer program implements the steps of the method for determining a fraction of blood flow reserve and variations thereof according to embodiments of the present disclosure.

[0073] While specific functions have been 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 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, a specific module performing an action may include the specific module performing the action itself and / or another module that the specific module calls or otherwise accesses to perform the action. For example, the various modules or units described according to one or more embodiments of this disclosure may be combined into a single module or unit in some embodiments. As another example, two or more modules or units may be described in parallel in one or more embodiments of this disclosure, while in other embodiments, these modules and units may have one or more inclusion relationships. As used herein, the phrase "entity A initiates action B" or "entity A causes action B to be performed" may refer to entity A issuing an instruction to perform action B, but entity A itself does not necessarily perform action B. For example, the phrase "display module causes display..." could mean that the display module instructs a display (not shown) or other possible display device to display, without the display module itself needing to perform the "display" action.

[0074] It should also be understood that this article can describe various technologies in the general context of software and hardware components or program modules. The above regarding... Figure 3The various modules described 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 code / instructions configured to execute in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules may be implemented as hardware logic / circuit. For example, in some embodiments, one or more of the modules or units described according to one or more embodiments of this disclosure may be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip (which includes a processor (e.g., a Central Processing Unit (CPU), microcontroller, microprocessor, digital signal processor (DSP), etc.), memory, one or more communication interfaces, and / or one or more other components of circuitry) and may optionally execute received program code and / or include embedded firmware to perform functions.

[0075] According to one aspect of this disclosure, a computing device is provided, including 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 of the method embodiments described above.

[0076] According to one aspect of this disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of any of the method embodiments described above.

[0077] According to one aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of any of the method embodiments described above.

[0078] In the following text, combined with Figure 4 Illustrative examples describing such computer devices, non-transitory computer-readable storage media, and computer program products.

[0079] Figure 4 An example configuration of a computer device 400 that can be used to implement the methods described herein is shown. For example, Figure 1 The server 120 and / or client device 110 shown may include an architecture similar to computer device 400. The aforementioned device / apparatus for determining fractional blood flow reserve may also be implemented wholly or at least partially by computer device 400 or similar device or system.

[0080] Computer device 400 can be a variety of different types of devices, such as a service provider's server, 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 400 include, but are not limited to: desktop computers, server computers, laptop or netbook computers, mobile devices (e.g., tablets, cellular or other wireless phones (e.g., smartphones), notebook computers, mobile stations), wearable devices (e.g., glasses, watches), entertainment devices (e.g., entertainment appliances, set-top boxes communicatively coupled to a display device, game consoles), televisions or other display devices, automotive computers, and so on. Therefore, the range of computer device 400 can be from full-resource devices with large amounts of memory and processor resources (e.g., personal computers, game consoles) to low-resource devices with limited memory and / or processing resources (e.g., traditional set-top boxes, handheld game consoles).

[0081] Computer device 400 may include at least one processor 402, memory 404, multiple communication interfaces 406, display device 408, other input / output (I / O) devices 410, and one or more mass storage devices 412 capable of communicating with each other, such as via system bus 414 or other suitable connections.

[0082] Processor 402 may be a single processing unit or multiple processing units, and all processing units may include single or multiple computing units or multiple cores. Processor 402 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, processor 402 may be configured to acquire and execute computer-readable instructions stored in memory 404, mass storage device 412, or other computer-readable media, such as program code of operating system 416, program code of application program 418, program code of other program 420, etc.

[0083] Memory 404 and mass storage device 412 are examples of computer-readable storage media for storing instructions executed by processor 402 to perform the various functions described above. For example, memory 404 may generally include both volatile and non-volatile memory (e.g., RAM, ROM, etc.). Furthermore, mass storage device 412 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., CDs, DVDs), storage arrays, network-attached storage, storage area networks, etc. Both memory 404 and mass storage device 412 may be collectively referred to herein as memory or computer-readable storage media, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code, which may be executed by processor 402 as a specific machine configured to perform the operations and functions described in the examples herein.

[0084] Multiple program modules may be stored on mass storage device 412. These programs include operating system 416, one or more application programs 418, other programs 420, and program data 422, and they may be loaded into memory 404 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 components / functions including method 200 (including any suitable steps of method 200) and / or other embodiments described herein.

[0085] Although Figure 4 The modules 416, 418, 420, and 422, or portions thereof, are illustrated as being stored in memory 404 of computer device 400; however, modules 416, 418, 420, and 422 may be implemented using any form of computer-readable medium accessible by computer device 400. As used herein, “computer-readable medium” includes at least two types of computer-readable media: computer storage media and communication media.

[0086] Computer storage media includes volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing 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 technologies, CD-ROM, DVD, or other optical storage devices, magnetic cassettes, magnetic tapes, disk storage devices or other magnetic storage devices, or any other non-transfer medium that can be used to store information for access by computer equipment.

[0087] In contrast, communication media can embody computer-readable instructions, data structures, program modules, or other data within modulated data signals such as carrier waves or other transmission mechanisms. Computer storage media as defined herein do not include communication media.

[0088] Computer device 400 may also include one or more communication interfaces 406 for exchanging data with other devices, such as via a network, direct connection, etc., as discussed above. Such communication interfaces can be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), wired or wireless (such as IEEE 802.11 Wireless LAN (WLAN)) wireless interface, Wi-MAX interface, Ethernet interface, Universal Serial Bus (USB) interface, cellular network interface, Bluetooth. TM Interfaces, near field communication (NFC) interfaces, etc. Communication interface 406 can facilitate communication across various network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. Communication interface 406 can also provide communication with external storage devices (not shown) such as storage arrays, network-attached storage, storage area networks, etc.

[0089] In some examples, a display device 408, such as a monitor, may be included for displaying information and images to the user. Other I / O devices 410 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 so on.

[0090] Although this disclosure has been described and illustrated in detail in the accompanying drawings and the foregoing description, such description and illustration should be considered illustrative and suggestive, not restrictive; this disclosure is not limited to the disclosed embodiments. By studying the drawings, the disclosure, and the appended claims, those skilled in the art will be able to understand and implement variations of the disclosed embodiments in practicing the claimed subject matter. In the claims, the word "comprising" does not exclude other elements or steps not listed, and the words "a" or "an" do 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 beneficial.

Claims

1. A method for determining fractional flow reserve, comprising: Acquire medical imaging data of the target blood vessel; Based on the medical imaging data, at least one region of interest that can cause pressure changes is identified; Identify at least one local pressure change index associated with each of the identified regions of interest; and The fractional flow reserve (FFR) value at at least one target location in the target vessel is determined based on the at least one local pressure change index.

2. The method according to claim 1, wherein, The region of interest includes at least one of the lesion region, the stenosis region, and the bifurcation region.

3. The method according to claim 2, wherein, Determining at least one local pressure change index associated with each of the identified at least one region of interest includes determining the at least one local pressure change index by means of a prediction model, wherein the type of the at least one region of interest is input into the prediction model.

4. The method according to any one of claims 1-3, wherein, Determining the fractional flow reserve (FFR) value at at least one target location within the target vessel based on the at least one local pressure change index includes: The FFR value at the target location is obtained based on the pressure change index before the target location from the at least one local pressure change index.

5. The method according to any one of claims 1-4, further comprising determining a background pressure change index, wherein, Determining the fractional flow reserve (FFR) value at at least one target location in the target vessel based on the at least one local pressure change index includes determining the FFR value based on the at least one local pressure change index and the background pressure change index.

6. The method according to any one of claims 1-5, wherein, Identifying at least one local pressure change index associated with each of the identified regions of interest includes: Based on medical imaging data of the local area corresponding to the at least one region of interest, determine the at least one local pressure change index.

7. The method according to claim 6, wherein, The size of the local region is determined based on the thickness of the blood vessel in which the corresponding region of interest is located.

8. The method according to any one of claims 1-7, wherein, The medical imaging data includes the topology of the target blood vessel, which is modeled using a graph convolutional network.

9. The method according to any one of claims 1-8, wherein, The medical imaging data includes multi-phase CT or CTA images at different cardiac cycles.

10. The method according to any one of claims 1-9, further comprising: Before determining the at least one local pressure change index, the vascular segment containing the region of interest is straightened.

11. The method according to any one of claims 1-10, wherein, The pressure change indicator is the pressure drop value or the change in FFR.

12. An apparatus for determining fractional flow reserve, comprising: The medical imaging data acquisition unit is used to acquire medical imaging data of the target blood vessel. A region of interest identification unit is used to identify at least one region of interest that can cause pressure changes based on the medical image data; A local pressure change index determination unit is used to determine at least one local pressure change index associated with each of the identified at least one region of interest; and A fractional flow reserve (FFR) determination unit is used to determine the fractional flow reserve (FFR) value at at least one target location in the target blood vessel based on the at least one local pressure change index.

13. A computing device, comprising: Memory, processor, and computer program stored on said memory, The processor is configured to execute the computer program to implement the steps of the method according to any one of claims 1-11.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-11.

15. A computer program product comprising a computer program, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-11.

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