CN117976233A - System and method for determining fractional flow reserve - Google Patents

Abstract

Embodiments of the present disclosure provide a system and method for determining fractional flow reserve. The method comprises the following steps: obtaining a first value of a default fractional flow reserve of a target blood vessel measurement point of a target object, wherein the target blood vessel measurement point is a point on a blood vessel of the target object, and the default fractional flow reserve is a fractional flow reserve when a physiological parameter has a default value; obtaining a query relationship indicating an alternative correspondence between physiological parameters and fractional flow reserve of a vascular measurement point when the default fractional flow reserve of the vascular measurement point is a different alternative value; and determining a second value of a target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship, the target fractional flow reserve being a fractional flow reserve at which the physiological parameter has a target value.

Description

System and method for determining fractional flow reserve

Technical Field

The present disclosure relates to the field of medical technology, and in particular, to a system and method for determining fractional flow reserve.

Background

Fractional flow reserve (Fractional Flow Reserve, FFR) can be effective in assessing the extent of ischemia due to plaque stenosis, providing a reference for the direction of subsequent treatment. Fractional flow reserve (FFR obtained based on CT may be referred to as FFRCT) obtained based on medical imaging techniques (e.g., coronary CT scanning) is more patient friendly than invasive FFR due to the non-invasive examination means it uses.

Existing methods of calculating FFR require a significant amount of computational resources and are inefficient. For example, FFR is calculated by performing simulation by computational fluid dynamics (Computational Fluid Dynamics, CFD) to obtain blood flow characteristics (pressure, velocity, etc.). The CFD simulation has the advantages of high accuracy, high authenticity and the like. However, at the same time, the principle is to solve the Navier-Stokes equation, and the process involves very complex calculation, so that the CFD simulation calculation cost is large and the time is long. For another example, FFR is obtained using a machine learning model. Since the input of the machine learning model needs to contain a large amount of data about the patient (e.g., physiological parameters, medical images, detailed anatomical features, etc.), a significant amount of computing resources still need to be expended and is inefficient.

Physiological parameters of the patient (blood pressure, heart rate, cardiac output, etc.) are directly related to the calculation of FFR. In practice, FFR is typically determined using the same set of default physiological parameters for different subjects. When the FFR of the subject is subsequently required to be determined again, the actual FFR will change due to the change in the physiological parameter of the subject, and thus if the FFR determined before is directly used, the diagnosis of the patient will be adversely affected. If FFR is calculated based on new physiological parameters, again using existing methods, this will result in inefficiency and a significant amount of time and computational resources.

It is therefore desirable to provide a system and method for efficiently and accurately determining fractional flow reserve.

Disclosure of Invention

One of the embodiments of the present specification provides a method of determining fractional flow reserve. The method comprises the following steps: obtaining a first value of a default fractional flow reserve of a target blood vessel measurement point of a target object, wherein the target blood vessel measurement point is a point on a blood vessel of the target object, and the default fractional flow reserve is a fractional flow reserve when a physiological parameter has a default value; obtaining a query relationship indicating an alternative correspondence between physiological parameters and fractional flow reserve of a vascular measurement point when the default fractional flow reserve of the vascular measurement point is a different alternative value; and determining a second value of a target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship, the target fractional flow reserve being a fractional flow reserve at which the physiological parameter has a target value.

One of the embodiments of the present description provides a system for determining fractional flow reserve. The system comprises an acquisition module configured to acquire a first value of a default fractional flow reserve of a target vascular measurement point of a target subject, wherein the target vascular measurement point is a point on a blood vessel of the target subject, and the default fractional flow reserve is a fractional flow reserve when a physiological parameter has a default value; the acquisition module is further configured to acquire a query relationship indicating an alternative correspondence between the physiological parameter and a fractional flow reserve of a vascular measurement point when the default fractional flow reserve is a different alternative value; and a determining module configured to determine a second value of a target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship, the target fractional flow reserve being a fractional flow reserve at which the physiological parameter has a target value.

One of the embodiments of the present description provides a system for determining fractional flow reserve. The system includes at least one memory device for storing computer instructions; at least one processor configured to execute the computer instructions to implement the method of determining fractional flow reserve described above.

Additional features of the application will be set forth in part in the description which follows. Additional features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following description and the accompanying drawings or may be learned from production or operation of the embodiments. The features of the present application can be implemented and obtained by practicing or using the various aspects of the methods, means, and combinations set forth in the detailed examples below.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic illustration of an application scenario of an exemplary FFR determination system shown in accordance with some embodiments of the present description;

FIG. 2 is a schematic diagram of an exemplary FFR determination system shown in accordance with some embodiments of the present description;

FIG. 3 is a schematic diagram of an exemplary FFR determination process shown in accordance with some embodiments of the present disclosure;

FIG. 4A is an exemplary query relationship 400 shown in accordance with some embodiments of the present description;

FIG. 4B is an exemplary alternative correspondence 430 shown in accordance with some embodiments of the present description;

FIG. 5 is a schematic diagram of an exemplary flow 500 for generating query relationships shown in accordance with some embodiments of the present description;

FIG. 6 is a schematic diagram of a distribution of FFR values corresponding to the same reference value of a physiological parameter when a default FFR is a different alternative value, according to an exemplary embodiment of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

Fig. 1 is a schematic illustration of an application scenario of an exemplary FFR determination system according to some embodiments of the present description. As shown in fig. 1, FFR determination system 100 may include a medical device 110, a processing device 120, a terminal device 130, a storage device 140, and a network 150. In some embodiments, the processing device 120 may be part of the medical device 110. The connections between components in FFR determination system 100 may be variable. As shown in fig. 1, medical device 110 may be connected to processing device 120 through a network 150. As another example, medical device 110 may be directly connected to processing device 120. For another example, the storage device 140 may be connected to the processing device 120 directly or through the network 150. As yet another example, terminal device 130 may be directly connected to processing device 120 (as indicated by the dashed arrow connecting terminal device 130 and processing device 120), or may be connected to processing device 120 via network 150.

Medical device 110 may be a non-invasive scanning imaging device for disease diagnosis or research purposes. In some embodiments, the medical device 110 may scan an object within a detection region or scanning region to obtain scan data for the object. In some embodiments, medical device 110 may include a single modality scanner and/or a multi-modality scanner. The single mode scanner may include, for example, an ultrasound scanner, an X-ray scanner, a Computed Tomography (CT) scanner, a Magnetic Resonance Imaging (MRI) scanner, an Optical Coherence Tomography (OCT) scanner, an Ultrasound (US) scanner, an intravascular ultrasound (IVUS) scanner, or the like, or any combination thereof. The multi-modality scanner may include, for example, an X-ray imaging-magnetic resonance imaging (X-ray-MRI) scanner, a positron emission tomography-X-ray imaging (PET-X-ray) scanner, a single photon emission computed tomography-magnetic resonance imaging (SPECT-MRI) scanner, a positron emission tomography-computed tomography (PET-CT) scanner, a digital subtraction angiography-magnetic resonance imaging (DSA-MRI) scanner, and the like. In some embodiments, the medical device 110 is a CT scanner. In some embodiments, the processing device 120 may be integrated on the medical device 110, or the medical device 110 and the processing device 120 may perform their functions by the same entity. The medical devices provided above are for illustrative purposes only and are not intended to limit the scope of the present description.

Processing device 120 may process data and/or information obtained from medical device 110, terminal device 130, storage device 140, or other components of FFR determination system 100. For example, the processing device 120 may obtain a first value of a default fractional flow reserve for a target vascular measurement point of a target subject. The default fractional flow reserve is the fractional flow reserve at which the physiological parameter has a default value. Further, processing device 120 may obtain the query relationship and determine a second value of the target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship. The query relationship indicates an alternative correspondence between the physiological parameter and the fractional flow reserve of the vascular measurement point when the default fractional flow reserve is a different alternative value. The fractional flow reserve target is the fractional flow reserve at which the physiological parameter has a target value.

In some embodiments, the processing device 120 may be local or remote. For example, the processing device 120 may access information and/or data from the medical device 110, the terminal device 130, and/or the storage device 140 via the network 150.

Terminal device 130 may include a mobile device 131, a tablet 132, a notebook 133, and the like, or any combination thereof. In some embodiments, the terminal device 130 may be part of the processing device 120.

Storage device 140 may store data, instructions, and/or any other information. In some embodiments, the storage device 140 may store data obtained from the medical device 110, the processing device 120, and/or the terminal device 130, e.g., medical images generated by the medical device 110, etc.

Network 150 may include any suitable network capable of facilitating the exchange of information and/or data. In some embodiments, at least one component of FFR determination system 100 (e.g., medical device 110, processing device 120, terminal device 130, storage device 140) may exchange information and/or data with at least one other component of FFR determination system 100 via network 150. For example, the processing device 120 may obtain medical images of the target object from the medical device 110 via the network 150. As another example, terminal device 130 may obtain FFR of the target object from processing device 120 via network 150.

It should be noted that FFR determination system 100 is provided for illustrative purposes only and is not intended to limit the scope of the present description. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, FFR determination system 100 may also include an input device and/or an output device. As another example, FFR determination system 100 may implement similar or different functionality on other devices. However, such changes and modifications do not depart from the scope of the present specification.

FIG. 2 is a schematic diagram of an exemplary FFR determination system shown in accordance with some embodiments of the present specification.

As shown in fig. 2, in some embodiments, FFR determination system 200 may include an acquisition module 201 and a determination module 202. In some embodiments, the corresponding functions of FFR determination system 200 may be performed by processing device 120, e.g., acquisition module 201 and determination module 202 may be modules in processing device 120.

The acquisition module 201 may be configured to acquire a first value of a default fractional flow reserve of a target vascular measurement point of a target subject. The target vascular measurement point is a point on a blood vessel of the target subject, and the default fractional flow reserve is a fractional flow reserve when the physiological parameter has a default value. For further description of the first value of the default fractional flow reserve for the target vascular measurement point of the target subject, see elsewhere in this specification (e.g., 301 in fig. 3), which is not described in detail herein.

The acquisition module 201 is further configured to acquire the query relationship. The query relationship indicates an alternative correspondence between the physiological parameter and the fractional flow reserve of the vascular measurement point when the default fractional flow reserve is a different alternative value. For more description of obtaining query relationships, see elsewhere in this document (e.g., 302 in fig. 3), and will not be described in detail herein.

The determination module 202 is configured to determine a second value of a target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship, the target fractional flow reserve having a fractional flow reserve at which the physiological parameter has a target value. For further description of determining the second value of the target fractional flow reserve for the target vascular measurement point, see elsewhere in this description (e.g., 303 in fig. 3), no further description is provided herein.

It should be understood that the system shown in fig. 2 and its modules may be implemented in a variety of ways. For example, in some embodiments the system and its modules may be implemented in hardware, software, or a combination of software and hardware.

It should be noted that the above description of the system and its modules is for descriptive convenience only and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. For example, in some embodiments, the above modules disclosed in fig. 2 may be different modules in one system, or may be one module to implement the functions of two or more modules described above. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

Fig. 3 is a schematic flow chart of an exemplary FFR determination shown in accordance with some embodiments of the present description. In some embodiments, one or more steps of flow 300 may be implemented at FFR determination system 100 shown in fig. 1 or performed by FFR determination system 200 shown in fig. 2. For example, the process 300 may be performed by a module of the processing device 120. As shown in fig. 3, the process 300 may include the following steps.

Step 301, obtaining a first value of a default fractional flow reserve of a target blood vessel measurement point of a target object, wherein the target blood vessel measurement point is a point on a blood vessel of the target object, and the default fractional flow reserve is a fractional flow reserve when a physiological parameter has a default value. In some embodiments, step 301 may be performed by processing device 120 or acquisition module 201.

The target object may comprise a human body, an animal or a part thereof. Hereafter, description will be made taking a human body as an example.

The blood vessel may include various blood vessels such as coronary artery, aorta, pulmonary artery, etc. The present application is not limited in the type of vessel.

The target vessel measurement point is a point on the vessel of the target object. The target vessel measurement may include one or more. In some embodiments, the target vascular measurement point comprises a point in the blood vessel near a lesion (e.g., thrombus). For example, the target vessel measurement point includes a point in the vessel a distance (e.g., 2 cm) upstream and downstream of the lesion. In some embodiments, the target vascular measurement point is specified by a user (e.g., a physician). In some embodiments, the processing device 120 may select a plurality of target vessel measurement points at equal intervals along the direction of extension of the vessel. Optionally, the target vascular measurement points near the user-specified location are more dense upstream and downstream of the lesion than the target vascular measurement points in other areas. For each target vascular measurement point, steps 301-303 may be performed to determine a second value of its corresponding target fractional flow reserve.

In some embodiments, the physiological parameter includes at least one of blood pressure, cardiac output, heart rate, and the like. Wherein the blood pressure may be the mean arterial pressure, i.e. the average of the sum of the systolic and diastolic pressures. The default value of the physiological parameter should be adapted to the physiological condition of most people and is thus typically chosen within the normal physiological parameters. For example, a large number of normal human physiological parameters may be counted and the mean or mode of these physiological parameters selected as a default value. Default values may also be determined empirically by the user. The application is not limited in this regard.

In some embodiments, a medical image of the target object may be acquired and a first value of the default FFR is determined using one of a computational fluid dynamics (Computational Fluid Dynamics, CFD) equation, a centralized parameter model, a machine learning model based on the medical image and the default value. The medical image includes a blood vessel of the target object. The medical image may be an image obtained by scanning a region of the human body containing blood vessels, for example, a CT angiographic image, a CT pan-scan image, or the like. In some embodiments, a scan may be performed on a blood vessel of a subject by medical device 110 to acquire a medical image. In some embodiments, the medical image may be generated in advance and stored in a storage device (e.g., storage device 140 or an external storage device) from which the processing device 120 may obtain the medical image.

Specifically, the processing device 120 determines a vessel segmentation image of the target object based on the medical image. The blood vessel segmentation image refers to an image generated after segmentation of blood vessels in the medical image, which may indicate blood vessels of the subject. In some embodiments, different vessel branches may be displayed in different ways in the vessel segmentation image. A vessel branch may include a segment of a vessel between two adjacent bifurcation points in the vessel, a segment of a vessel between a vessel start point and its adjacent bifurcation point, and a segment of a vessel between each vessel end and its adjacent bifurcation point. For example, different blood vessel branches may be displayed in the blood vessel segmentation image using different colors. For another example, different branch labels (e.g., labels "1", "2", …) may be used to display different vessel branches in the vessel segmentation image. In some embodiments, the vessel segmentation image may be derived by a user (e.g., a imaging physician) manually delineating the vessel from the medical image. In some embodiments, the vessel segmentation image may be automatically derived by the processing device 120 from segmenting the vessel from the medical image. For example, the processing device 120 may segment blood vessels from medical images using an image segmentation algorithm or a machine learning model to obtain vessel segmentation images.

The processing device 120 then performs a three-dimensional reconstruction based on the medical image and the vessel segmentation image, resulting in a three-dimensional model of the vessel, and determines a first value of the default FFR based on the three-dimensional model. Specifically, in some embodiments, the processing device 120 may determine a default boundary condition for the target object based on the default value. Boundary conditions in the present application refer to boundary conditions of blood vessels, which are directly related to physiological parameters of the patient (blood pressure, heart rate, cardiac output, etc.). Further, processing device 120 may determine a first value of the default FFR based on a default boundary condition of the target object using one of a computational fluid dynamics CFD equation, a centralized parameter model, a machine learning model, and the like. For example, first, the processing device 120 determines an aortic inlet boundary condition (e.g., a heart cycle of 1 second, a cardiac output of 83 ml/s) of the target object, an aortic outlet boundary condition resistance, and determines a boundary condition resistance of a blood vessel of the target object according to a predetermined boundary condition resistance equation. For example, the processing device 120 may determine the aortic outlet boundary condition resistance and the boundary condition resistance of the blood vessel according to the following formulas (1) and (2), respectively:

wherein, R ₁ is the aortic outlet boundary condition resistance, R ₂ is the boundary condition resistance of one lumen of the target vessel (one lumen of the vessel refers to the vessel segment through which blood passes from the beginning of the inflow vessel to one end of the outflow vessel), P _mean is the blood pressure, Q is the cardiac output, i is the lumen number of the vessel, n is the total number of lumens, l _i is the branch vessel weight corresponding to the i-th lumen end, l _e is the branch vessel weight corresponding to the current lumen end, and β and γ are hyper-parameters.

The processing device 120 then determines an equivalent voltage for each point on the vessel of the target object using the CFD equation based on the aortic inlet boundary condition, the aortic outlet boundary condition, and the boundary condition resistance of the vessel of the target object. Finally, processing device 120 determines a first value of a default FFR for each point on the vessel by normalizing the equivalent voltage for each point on the vessel based on the vessel inlet equivalent voltage and obtains a first value of a default FFR for each target vessel measurement point.

For another example, the processing device 120 determines an equivalent resistance of the blood vessel based on the medical image using a first machine learning model and determines a boundary condition resistance of the blood vessel using a second machine learning model. Wherein the equivalent resistance of the vessel comprises resistance values of a plurality of points on the vessel, and the boundary condition resistance is used for describing boundary conditions of blood flowing out of the end of the vessel. Further, the processing device 120 determines a first value of the default FFR for each point on the vessel based on the equivalent resistance and the boundary condition resistance of the vessel, and obtains a first value of the default FFR for each target vessel measurement point.

Step 302, obtaining a query relationship indicating an alternative correspondence between the physiological parameter and the fractional flow reserve of the blood vessel measurement point when the default fractional flow reserve is a different alternative value. In some embodiments, step 302 may be performed by processing device 120 or acquisition module 201.

The alternative value refers to a value to which the default fractional flow reserve of the vascular measurement site may be equal. The correspondence between the physiological parameter and the fractional flow reserve may reflect the value corresponding to the fractional flow reserve of the physiological parameter at the vascular measurement site when it has different values. It should be appreciated that when the default fractional flow reserve of different vascular measurement points have different alternative values, the vascular measurement points have different characteristics, and the correspondence between their physiological parameters and fractional flow reserve (referred to as alternative correspondence in this specification) is also different.

In some embodiments, the query relationship includes a multi-dimensional table, a multi-dimensional matrix, a dictionary, and the like. For example, FIG. 4A is an exemplary query relationship 400 shown in accordance with some embodiments of the present description. As shown in FIG. 4A, the query relationship 400 is a multi-dimensional table. Query relationship 400 may indicate an alternative correspondence between physiological parameters and fractional flow reserve of a vascular measurement point when its default FFR is a different alternative value. For example, query relationship 400 may include alternative correspondence 410 (i.e., 2D table 410) when default FFR is 0.9 (i.e., when alternative value is 0.9), alternative correspondence 420 when default FFR is 0.8 (i.e., when alternative value is 0.8), alternative correspondence 430 when default FFR is 0.7 (i.e., when alternative value is 0.7), and so forth. Wherein each alternative correspondence is represented as a 2D table, wherein the horizontal axis of the 2D table represents cardiac output, the vertical axis represents blood pressure, and the value of each element in the 2D table represents FFR values under the corresponding physiological parameter. For example, in the 2D table 410, the FFR value corresponding to the cardiac output of 66ml/s and the blood pressure of 70mmHg is 0.90, and the FFR value corresponding to the cardiac output of 116ml/s and the blood pressure of 77mmHg is 0.83. This means that for a vascular measurement point with a default FFR value of 0.9, the FFR value is 0.90 when the cardiac output is 66ml/s and the blood pressure is 70 mmHg; the FFR value was 0.83 when the cardiac output was 116ml/s and the blood pressure was 77 mmHg.

In some embodiments, alternative correspondence may also be represented by other forms. For example, fig. 4B is an exemplary alternative correspondence 430 shown in accordance with some embodiments of the present description. As shown in fig. 4B, the alternative correspondence 430 is represented by a grid map of three-dimensional coordinates. Wherein the three axes of the three-dimensional coordinates represent the output, blood pressure, and FFR values, respectively. As shown in fig. 4B, the higher the cardiac output, the lower the FFR; the higher the blood pressure, the higher the FFR.

In some embodiments, the query relationship may be generated in advance and stored in a storage device (e.g., storage device 140), from which the processing device 120 directly retrieves the query relationship.

In some embodiments, the processing device 120 may obtain a plurality of reference values and default values for the physiological parameter. For each reference object of the plurality of reference objects, processing device 120 may determine a third value of a default FFR and a fourth value of the plurality of reference FFR for each vessel measurement point of the reference object. The fractional flow reserve when each reference FFR is a reference value of the plurality of reference values for the physiological parameter. Further, the processing device 120 may determine the query relationship based on the plurality of reference values, the third value and the fourth value for each vascular measurement point for each reference object. For more description of determining query relationships, please refer to fig. 5 and related descriptions thereof, which are not repeated here.

Step 303, determining a second value of a target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship, the target fractional flow reserve having a fractional flow reserve at which the physiological parameter has a target value. In some embodiments, step 303 may be performed by processing device 120 or determination module 202.

In some embodiments, the processing device 120 may determine the second value of the target fractional flow reserve by looking up the query relationship directly from the first value, the target physiological parameter. In some embodiments, for each target vascular measurement point, processing device 120 may determine one or more reference alternative correspondences between the physiological parameter and the fractional flow reserve based on the query relationship and the first value for that target vascular measurement point. The reference candidate correspondence is a candidate correspondence selected from a plurality of candidate correspondences of the query relationship. The difference between the alternative value of the default fractional flow reserve corresponding to each reference alternative correspondence and the first value satisfies a preset condition. The preset condition may be a default condition of FFR determination system 100 or automatically determined by processing device 120. Alternatively, the preset condition may be manually determined by a user (e.g., doctor). For example, the preset condition is that the difference between the alternative value and the first value of the default fractional flow reserve corresponding to each reference alternative correspondence is less than 0.1. For another example, the preset condition is that the difference between the alternative value of the default fractional flow reserve corresponding to the reference alternative correspondence and the first value is the smallest of all the alternative correspondences. Further, processing device 120 may determine a second value of the target fractional flow reserve for the target vascular measurement point based on the one or more reference candidate correspondences.

If there is only one reference candidate correspondence, processing device 120 may look up a second value of the target FFR from the reference candidate correspondence based on the target physiological parameter. For example, as shown in FIG. 4A, if the reference alternative relationship is a 2D table 410 corresponding to when the default FFR is 0.9. The target physiological parameter is cardiac output 86, blood pressure 84, then a second value of 0.88 for the target FFR for the target measurement point can be determined by looking up the 2D table 410.

If the reference candidate correspondence is plural, the processing device 120 may determine a reference second value of the target FFR from each reference candidate correspondence according to the target physiological parameter. Further, processing device 120 may determine the second value of the target FFR by interpolation (e.g., linear interpolation, bilinear interpolation, trilinear interpolation, etc.) based on the alternative value of the default FFR and the reference second value, the first value, for each reference alternative correspondence.

In some embodiments, for each reference candidate correspondence, the processing device 120 may determine one or more reference target physiological parameters from the reference candidate correspondence if the target physiological parameters are not included in the reference candidate correspondence. The difference value between each reference target physiological parameter and the target physiological parameter meets the preset condition. The preset condition may be a default condition of FFR determination system 100 or automatically determined by processing device 120. Alternatively, the preset condition may be manually determined by a user (e.g., doctor). For example, the preset condition is that each reference target physiological parameter differs from the target physiological parameter by less than 2. For another example, the preset condition is that the reference target physiological parameter is closest to the target physiological parameter, i.e., the difference between the reference target physiological parameter and the target physiological parameter is the smallest of the differences between all the reference physiological parameters and the target physiological parameter. Further, processing device 120 may determine a reference second value of the target FFR from the reference alternative correspondence based on the one or more reference target physiological parameters. If there is only one reference target physiological parameter, processing device 120 may look up a reference second value of the target FFR from the reference alternative correspondence based on the reference target physiological parameter. If the reference target physiological parameter is multiple, the processing device 120 can determine a reference second value of the target FFR by interpolation (e.g., linear interpolation, bilinear interpolation, trilinear interpolation, etc.) from the FFR values corresponding to each of the multiple reference target physiological parameters.

It should be noted that the above description of the process 300 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.

FIG. 5 is a schematic diagram of an exemplary flow 500 for generating query relationships, shown in accordance with some embodiments of the present description. In some embodiments, the process 500 may be performed by the processing device 120 or the first one or more modules shown in fig. 2 (e.g., the acquisition module 201). As shown in fig. 5, the process 500 may include the following steps.

Step 501, a plurality of reference values and default values of a physiological parameter are obtained.

In some embodiments, the plurality of reference values are within a range of physiological parameters of a normal person. In some embodiments, the plurality of reference values may be random values within a range of normal physiological parameters. In some embodiments, the plurality of reference values of the physiological parameter may be determined by sampling at regular, e.g., equidistant, intervals. For example, if the blood pressure of a normal person ranges from 70mmHg to 105mmHg, a reference value of the blood pressure is determined every 5mmHg from 70mmHg to 105 mmHg. The range of the cardiac output of the normal person is 66-116mL/s, and the cardiac output is from 66mL/s to 116mL/s, and a reference value of the cardiac output is determined every 5 mL/s. Thus, 8 blood pressure reference values and 11 cardiac output reference values were obtained. The reference values of 8 groups of blood pressure and the reference values of 11 groups of cardiac output are combined pairwise, and 88 groups of reference values of physiological parameters can be obtained.

The relevant description of the default values of the physiological parameters refers to step 302 and will not be described in detail here.

Step 502, for each reference object of a plurality of reference objects, determining a third value of a default fractional flow reserve for each reference vascular measurement point of the reference object and a fourth value of a plurality of reference fractional flow reserve, each reference fractional flow reserve having a fractional flow reserve at one of the plurality of reference values for the physiological parameter. For a plurality of reference values, a fourth value of the reference fractional flow reserve corresponding to each reference value, i.e. the value of FFR when the physiological parameter is a certain reference value, needs to be determined.

The type of the reference object is the same as the target object. For example, the reference object and the target object are both human or the same animal. In some embodiments, for each reference object, the processing device 120 may acquire a reference medical image of the reference object. The reference medical image contains a reference vessel of the reference object. The reference vessel is the same vessel type as the target object in fig. 3. For example, both the reference vessel and the vessel of the target object are coronary. In some embodiments, the reference vessel of the portion of the reference subject contains a lesion therein. Further, the processing device 120 may determine a reference vessel segmentation image of the reference object based on the reference medical image and reconstruct a three-dimensional model of the reference vessel of the reference object further based on the reference vessel segmentation image. The processing device 120 may determine a plurality of reference vessel measurement points for the reference vessel based on the three-dimensional model of the reference vessel. In some embodiments, the reference vessel measurement points include points near a lesion in the reference vessel. For example, the reference vessel measurement point includes a point in the reference vessel a distance (e.g., 2 cm) upstream and downstream of the lesion. In some embodiments, the reference vascular measurement point is specified by a user (e.g., a physician). In some embodiments, the processing device 120 may select a plurality of reference vessel measurement points equidistant along the reference vessel. Optionally, the reference vessel measurement points near the user-specified location are more dense upstream and downstream of the lesion than the reference vessel measurement points in other areas.

Further, for each reference object, the processing device 120 determines a third value of a default FFR for each reference vascular measurement point of the reference object using the default value of the physiological parameter, and determines a fourth value of a reference FFR for each reference vascular measurement point of the reference object at the reference value using each reference value of the physiological parameter. In some embodiments, processing device 120 may determine a third value of the default FFR and a fourth value of the reference FFR for each reference object in a manner similar to the determination of the first value of the default FFR for the target object described in step 302. For example, processing device 120 may determine a default boundary condition of the reference object based on the default value and determine a third value of a default FFR of the reference object based on the default boundary condition of the reference object using one of a computational fluid dynamics CFD equation, a focused parameter model, a machine learning model, and the like. Similarly, for example, for each reference value, the processing device 120 may determine a reference boundary condition of the reference object based on the reference value, and determine a fourth value of a reference FFR of the reference object based on the reference boundary condition of the reference object using one of a computational fluid dynamics CFD equation, a centralized parametric model, a machine learning model, and the like. For example, for 88 sets of reference values and default values for 50 reference subjects and physiological parameters, the processing device 120 needs to perform 50 x 89 calculations by CFD equations, centralized parameter models, machine learning models, etc. using 88 sets of reference values and default values to obtain a third value for the default FFR and a fourth value for the reference FFR for all reference subjects.

In step 503, a query relationship is determined based on the plurality of reference values, the third value and the fourth value for each vascular measurement point for each reference object.

In some embodiments, processing device 120 may determine a plurality of alternative values corresponding to the plurality of alternative correspondence relationships, and a set of reference vessel measurement points corresponding to each alternative value, based on the third value of each reference vessel measurement point of each reference object. In some embodiments, processing device 120 may determine different values for all third values, each value of the third values being designated as an alternative value. In some embodiments, processing device 120 may designate a portion of the different values of the third value as alternative values. For example, the processing device 120 may select a partial value from among different values of the third value as an alternative value, e.g., 0.9, 0.8, 0.7, in an equally spaced or substantially equally spaced manner. The processing device 120 may further determine a set of reference vessel measurement points for each alternative value. In some embodiments, the third values of the set of reference vessel side volume points corresponding to the alternative values are each equal to the alternative value. For example, if the third values of the reference blood vessel measurement points A1, A2, A3 are each 0.7, the third values of the reference blood vessel measurement points B1, B2, B3 are each 0.75, and the third values of the reference blood vessel measurement points C1, C2, C3 are each 0.85, the reference blood vessel measurement points A1, A2, A3 are a set of reference blood vessel measurement points corresponding to the alternative value of 0.7, the reference blood vessel measurement points B1, B2, B3 are a set of reference blood vessel measurement points corresponding to the alternative value of 0.75, and the reference blood vessel measurement points C1, C2, C3 are a set of reference blood vessel measurement points corresponding to the alternative value of 0.85. In some embodiments, a reference vascular measurement point may be considered to correspond to an alternative value if the third value of the reference vascular measurement point is closest to the alternative value of all alternative values. For example, if the third value of the reference vascular measurement point A1 is 0.71, the alternative values are 0.7, 0.8 and 0.9, the reference vascular measurement point a corresponds to the alternative value of 0.7.

Further, for each alternative value, the processing device 120 may determine an alternative correspondence corresponding to the alternative value based on the fourth value of the set of reference vessel measurement points corresponding to the alternative value and the plurality of reference values. Specifically, for each reference value, processing device 120 may determine a fourth value corresponding to the reference value from the fourth values of the set of reference vessel measurement points corresponding to the alternative value, and determine an FFR value corresponding to the reference value in the alternative correspondence based on the fourth value corresponding to the reference value.

In some embodiments, processing device 120 may directly designate the fourth value of the set of reference vessel measurement points as the FFR value corresponding to the reference value in the alternative correspondence. For example, when the alternative value is 0.7, the fourth value of the reference blood vessel measurement points A1, A2, A3 is 0.81, 0.82 respectively at the first reference value (for example, 66ml/s of cardiac output and 70mmHg of blood pressure), the FFR value corresponding to the first reference value in the alternative correspondence corresponding to the alternative value 0.7 is 0.81, 0.82. The fourth values of the reference vascular measurement points A1, A2, A3 at the second reference values (e.g. the cardiac output pressure 76ml/s and the blood pressure 70 mmHg) are 0.83, 0.832, 0.835, respectively, the FFR values corresponding to the second reference values in the alternative correspondence corresponding to the alternative value 0.7 are 0.83, 0.832, 0.835.

In some embodiments, for each reference value, processing device 120 may determine a representative value of a reference FFR to which the reference value corresponds based on a set of fourth values to which the reference value corresponds, and determine an alternative correspondence to which the alternative value corresponds based on the representative value of the reference FFR to which each reference value corresponds. Specifically, the processing device 120 may determine a representative value corresponding to each reference value as the FFR value corresponding to the reference value in the alternative correspondence. For example, as shown in fig. 4A, FFR values in alternative correspondences of query relationship 400 are all representative values corresponding to different reference values. For example, for one reference value, the processing device 120 may designate, as the representative value, the fourth value that occurs the most times or an average value of the fourth values among the fourth values corresponding to the reference value. For example, as described above, when the alternative value is 0.7, the fourth values of the reference blood vessel measurement points A1, A2, A3 at the first reference value are 0.81, 0.82, respectively, and the FFR value corresponding to the first group of reference values in the alternative correspondence corresponding to the alternative value 0.7 is 0.82.

FIG. 6 is a schematic diagram of a distribution of FFR values corresponding to the same reference value of a physiological parameter when a default FFR is a different alternative value, according to an exemplary embodiment of the present disclosure. The reference value for blood pressure in the physiological parameter was 105mmHg and the reference value for cardiac output was 66mL/s. As shown in fig. 6, the comparison set of point distributions corresponding to the same alternative value of the default FFR illustrates that the fourth value of the reference FFR for a set of reference vessel measurement points corresponding to the same reference value is less different at the alternative value of the same default FFR. Thus, in some embodiments, for a reference value, the processing device 120 may designate as a representative value an average of the fourth values corresponding to the reference value. For example, as described above, when the alternative value is 0.7, the fourth values of the reference blood vessel measurement points A1, A2, A3 are 0.83, 0.832, 0.835, respectively, at the second reference value, and then the representative values corresponding to the second reference value in the alternative correspondence relation corresponding to the alternative value 0.7 are the average value 0.832 of 0.83, 0.832, 0.835. An accurate candidate object relationship can be obtained by taking the average value of the fourth values corresponding to the reference values as the representative value, so that an accurate FFR value can be obtained by searching the query relationship in actual application.

In some embodiments of the present description, a second value of the target fractional flow reserve for the target vascular measurement point of the target subject may be determined based on the query relationship and a first value of the default FFR for the target measurement point of the target subject. Possible benefits of embodiments of the present description include, but are not limited to: (1) In the prior art, after the physiological parameter of the target object is changed, the FFR determined by the default physiological parameter is still used as the FFR of the current state of the target object, compared with the FFR of the current state of the target object, the target blood flow reserve fraction obtained by the method is the target blood flow reserve fraction obtained based on the actual physiological parameter of the target object (namely, under the target physiological parameter), so that the FFR of the target object is more in line with the current physiological condition of the target object, and the accuracy is higher; (2) In some prior art, when the physiological parameters of the target object are changed, based on the new physiological parameters, the new FFR is determined again by the existing FFR calculation method (e.g. CFD simulation, centralized parameter model, existing machine learning model), which requires processing a large amount of data, resulting in inefficiency and consuming a large amount of time and computing resources. Compared with the method, the blood flow reserve score when the physiological parameter has the target value can be obtained directly by searching the query relation, the processing process is simple and quick, the efficiency of obtaining the blood flow reserve score can be greatly improved, and the computing resource is saved; (3) The query relation obtained by the application can be suitable for any object containing the same type of blood vessel, and has wide application range.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (10)

1. A method of determining fractional flow reserve, the method comprising:

Obtaining a first value of a default fractional flow reserve of a target blood vessel measurement point of a target object, wherein the target blood vessel measurement point is a point on a blood vessel of the target object, and the default fractional flow reserve is a fractional flow reserve when a physiological parameter has a default value;

Obtaining a query relationship indicating an alternative correspondence between physiological parameters and fractional flow reserve of a vascular measurement point when the default fractional flow reserve of the vascular measurement point is a different alternative value; and

A second value of a target fractional flow reserve for the target vascular measurement point is determined based on the first value and the query relationship, the target fractional flow reserve being the fractional flow reserve when the physiological parameter has a target value.

2. The method of claim 1, wherein the determining a second value of a target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship comprises:

Determining one or more reference alternative correspondence between the physiological parameter and the fractional flow reserve based on the query relationship and the first value, the difference between the alternative value of the default fractional flow reserve and the first value corresponding to each reference alternative correspondence satisfying a preset condition;

a second value of a target fractional flow reserve for the target vascular measurement point is determined based on the one or more reference candidate correspondences.

3. The method of claim 1, wherein the query relationship is determined by:

acquiring a plurality of reference values and the default values of the physiological parameters;

determining, for each of a plurality of reference subjects, a third value of a default fractional flow reserve for each reference vascular measurement point of the reference subject and a fourth value of a plurality of reference fractional flow reserve, each of the reference fractional flow reserve being a fractional flow reserve when the physiological parameter has one of the plurality of reference values; and

The query relationship is determined based on the plurality of reference values, the third value and the fourth value for each reference vessel measurement point for each reference object.

4. The method of claim 3, wherein determining the query relationship based on the plurality of reference values, the third value and the fourth value for each vascular measurement point of each reference object comprises:

Determining a plurality of alternative values corresponding to the plurality of alternative correspondence relationships and a set of reference vessel measurement points corresponding to each of the alternative values based on a third value of each reference vessel measurement point of each reference object;

For each of the candidate values, determining a candidate correspondence corresponding to the candidate value based on a fourth value of a set of reference vessel measurement points corresponding to the candidate value and the plurality of reference values.

5. The method of claim 4, wherein for each of the alternative values, determining an alternative correspondence corresponding to the alternative value based on a fourth value of a set of reference vessel measurement points corresponding to the alternative value and the plurality of reference values comprises:

for each of the said alternative values,

For each reference value, determining a fourth value corresponding to the reference value from fourth values of a set of reference vessel measurement points corresponding to the alternative value;

for each reference value, determining a representative value of a reference fractional flow reserve corresponding to the reference value based on a fourth value corresponding to the reference value; and

And determining an alternative corresponding relation corresponding to the alternative value based on the representative value of the reference blood flow reserve fraction corresponding to each reference value.

6. The method of claim 1, wherein the first value of the default fractional flow reserve is determined by:

Acquiring a medical image of the target object, the medical image comprising the blood vessel of the target object;

the first value is determined using one of a computational fluid dynamics equation, a centralized parameter model, a machine learning model based on the medical image and the default value.

7. The method of claim 1, wherein the query relationship comprises one or more of a multi-dimensional table, a multi-dimensional matrix, a dictionary.

8. The method of claim 1, wherein the physiological parameter comprises at least one of blood pressure, cardiac output, heart rate.

9. A system for determining fractional flow reserve, comprising:

An acquisition module configured to acquire a first value of a default fractional flow reserve of a target vascular measurement point of a target subject, wherein the target vascular measurement point is a point on a blood vessel of the target subject, and the default fractional flow reserve is a fractional flow reserve when a physiological parameter has a default value;

The acquisition module is further configured to acquire a query relationship indicating an alternative correspondence between the physiological parameter and a fractional flow reserve of a vascular measurement point when the default fractional flow reserve is a different alternative value; and

A determination module configured to determine a second value of a target fractional flow reserve for the target vascular measurement point based on the first value and the query relationship, the target fractional flow reserve being a fractional flow reserve at which the physiological parameter has a target value.

10. A system for determining fractional flow reserve, comprising:

at least one memory device for storing computer instructions;

At least one processor configured to execute the computer instructions to implement the method of any one of claims 1-8.