A blood flow mechanical energy loss determination method and device, electronic equipment and medium

Abstract

This invention discloses a method, apparatus, electronic device, and medium for determining blood flow mechanical energy loss. By acquiring the vascular input information of the blood vessel segment to be analyzed, the vascular input information is input into a pre-trained velocity distribution coefficient prediction model to obtain the output velocity distribution coefficient of the blood vessel segment to be analyzed. Based on the vascular input information and the output velocity distribution coefficient, the mechanical energy loss value of the blood vessel segment to be analyzed is obtained. Calculating the mechanical energy loss value of the blood vessel segment based on the vascular input information and the output velocity distribution coefficient solves the problems of existing mechanical energy loss calculation formulas failing to reflect downstream mechanical energy loss, having low accuracy, and large calculation errors, thus improving the accuracy of energy loss calculation.

Description

Technical Field

[0001] The embodiments of the present invention relate to hemodynamic simulation calculation technology, and in particular to a method, device, electronic device and medium for determining blood flow mechanical energy loss. Background Technology

[0002] With the improvement of living standards, vascular diseases are increasingly becoming a threat to human health, such as coronary artery disease (CAD), cerebrovascular disease, and aortic disease. For the assessment of blood flow disorders, functional parameters such as free flow rate (FFR), blood flow velocity, and blood pressure drop, as well as hemodynamic parameters, are becoming increasingly important in the diagnostic evaluation of these diseases.

[0003] Invasive examinations are limited in their use due to their high cost and the potential allergies of certain medications to some patients. In recent years, non-invasive functional assessment based on CTA has gained increasing attention. In non-invasive functional assessment, hemodynamics is mostly solved using 3D CFD methods to solve fluid dynamics equations. 3D CFD methods offer high accuracy and provide the most comprehensive and complete blood flow parameters. However, their computational efficiency is relatively low, and the computation time is long, limiting their application scenarios. Therefore, functional assessment methods based on reduced-order models have been proposed. However, while reduced-order model-based hemodynamic calculation methods improve computational efficiency, the lack of some geometric information necessitates coupling empirical formulas to calculate hemodynamic losses in key areas.

[0004] However, these empirical formulas can only assess the mechanical energy loss at locations of dramatic geometric changes in the vascular lumen (such as contraction and expansion, bifurcation, and tortuosity). In reality, the additional mechanical energy loss due to geometric changes is not only reflected at these sites of dramatic change but also affects the mechanical energy loss of subsequent vascular segments by influencing the flow velocity distribution across the vessel cross-section. Existing formulas for calculating mechanical energy loss (empirical formulas or other regression models (including machine learning models)) cannot reflect the mechanical energy loss downstream. Using such formulas to calculate mechanical resistance inevitably introduces errors. Summary of the Invention

[0005] This invention provides a method for determining blood flow mechanical energy loss, thereby improving the calculation accuracy of blood flow mechanical energy loss.

[0006] In a first aspect, embodiments of the present invention provide a method for determining blood flow mechanical energy loss, including:

[0007] For the blood vessel segment to be analyzed, obtain the blood vessel input information of the blood vessel segment to be analyzed;

[0008] The blood vessel input information is input into a pre-trained flow velocity distribution coefficient prediction model to obtain the output flow velocity distribution coefficient of the blood vessel segment to be analyzed.

[0009] The blood vessel input information and the output velocity distribution coefficient are input into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

[0010] Optionally, the method for determining blood flow mechanical energy loss further includes:

[0011] The blood vessel to be analyzed is segmented to obtain multiple blood vessel segments to be analyzed. The blood vessel input information of the blood vessel segment to be analyzed includes blood flow information, input end velocity distribution coefficient and blood vessel segment geometric parameters. The input end velocity distribution coefficient is the initial velocity distribution coefficient or the output end velocity distribution coefficient of the previous blood vessel segment to be analyzed.

[0012] Correspondingly, the sum of the mechanical energy loss values ​​of all the blood vessel segments to be analyzed is the mechanical energy loss value of the blood vessel to be analyzed.

[0013] Optionally, the method for determining blood flow mechanical energy loss further includes:

[0014] Obtain the input and output pressures of the blood vessel segment to be analyzed;

[0015] Energy conservation verification is performed based on the blood flow information, velocity distribution coefficient, input pressure, output pressure, and mechanical energy loss value of the blood vessel segment to be analyzed.

[0016] If the energy conservation verification fails, the vascular input information of the blood vessel segment to be analyzed is updated, and the mechanical energy loss value of the blood vessel segment to be analyzed is re-determined based on the updated vascular input information.

[0017] Optionally, the energy conservation verification based on the blood flow information, velocity distribution coefficient, input pressure, output pressure, and mechanical energy loss value of the blood vessel segment to be analyzed includes:

[0018] Blood kinetic energy is determined based on blood flow information and velocity distribution coefficient;

[0019] The input mechanical energy is determined based on the input pressure and the blood flow energy, and the output mechanical energy is determined based on the output pressure and the blood flow energy.

[0020] Based on the energy difference between the input mechanical energy and the output mechanical energy, the energy loss value of the blood vessel segment to be analyzed is compared with that of the input mechanical energy to determine whether energy conservation is satisfied.

[0021] Optionally, updating the vascular input information of the vascular segment to be analyzed includes:

[0022] Based on the comparison between the energy difference and the mechanical energy loss value of the blood vessel segment to be analyzed, the adjustment direction and / or adjustment value of the blood vessel input information are determined, and the blood vessel input information of the blood vessel segment to be analyzed is updated based on the adjustment direction and / or adjustment value.

[0023] Optionally, the method for obtaining the velocity distribution coefficient prediction model includes:

[0024] A sample blood vessel is acquired, and a three-dimensional blood flow simulation is performed on the sample blood vessel. The sample blood vessel is segmented, and the blood flow velocity distribution, blood flow information, and geometric parameters of each blood vessel segment at the inlet and outlet sections are extracted.

[0025] Based on the blood flow velocity distribution of each sample blood vessel segment at the inlet and outlet sections, the velocity distribution coefficient of each sample blood vessel segment at the inlet and outlet sections is determined. Based on the velocity distribution coefficient of each sample blood vessel segment at the inlet and outlet sections, blood flow information, and geometric parameters of the blood vessel segment set, multiple sets of first sample data are formed.

[0026] A flow velocity distribution coefficient prediction model is obtained based on the regression of the first sample data.

[0027] Optionally, the mechanical energy loss prediction model includes:

[0028] Based on the blood flow velocity distribution and blood flow information and blood flow pressure at the inlet and outlet sections, the mechanical energy at the inlet and outlet sections was determined, and the mechanical energy loss of each sample blood vessel segment was obtained.

[0029] Multiple sets of second sample data are generated based on the flow velocity distribution coefficient, blood flow information, geometric parameters of the blood vessel segment, and corresponding mechanical energy loss at the inlet and outlet sections of each sample blood vessel segment.

[0030] A mechanical energy loss prediction model is obtained based on the regression of the second sample data.

[0031] Secondly, embodiments of the present invention also provide a device for determining blood flow mechanical energy loss, comprising:

[0032] The input information acquisition module is used to acquire the vascular input information of the vascular segment to be analyzed.

[0033] The flow velocity distribution coefficient calculation module is used to input the blood vessel input information into a pre-trained flow velocity distribution coefficient prediction model to obtain the output flow velocity distribution coefficient of the blood vessel segment to be analyzed.

[0034] The mechanical energy loss calculation module is used to input the blood vessel input information and the output flow velocity distribution coefficient into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

[0035] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0036] At least one processor; and

[0037] A memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to implement the method for determining blood flow mechanical energy loss as described in any one of the first aspects.

[0039] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute the method for determining blood flow mechanical energy loss as described in any one of the first aspects.

[0040] This invention acquires the vascular input information of the blood vessel segment to be analyzed, inputs the vascular input information into a pre-trained velocity distribution coefficient prediction model to obtain the output velocity distribution coefficient of the blood vessel segment to be analyzed, and inputs the mechanical energy loss prediction model based on the vascular input information and the output velocity distribution coefficient to obtain the mechanical energy loss value of the blood vessel segment to be analyzed. Based on the vascular input information and the output velocity distribution coefficient, the mechanical energy loss value of the blood vessel segment to be analyzed is calculated. This solves the problem that the existing mechanical energy loss calculation formula cannot reflect the mechanical energy loss at the downstream end, has low accuracy, and has large calculation errors, thereby improving the accuracy of energy loss calculation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a method for determining blood flow mechanical energy loss provided in Embodiment 1 of the present invention;

[0043] Figure 2 This is a flowchart of a method for determining blood flow mechanical energy loss provided in Embodiment 2 of the present invention;

[0044] Figure 3 This is a schematic diagram of a blood flow mechanical energy loss device provided in Embodiment 3 of the present invention;

[0045] Figure 4This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0047] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0048] Example 1

[0049] Figure 1 This is a flowchart of a method for determining blood flow mechanical energy loss according to Embodiment 1 of the present invention. This embodiment is applicable to calculating the blood flow mechanical energy loss in blood vessels. This method can be executed by a blood flow mechanical energy loss determining device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0050] S110. For the blood vessel segment to be analyzed, obtain the blood vessel input information of the blood vessel segment to be analyzed.

[0051] The vessel segment to be analyzed can be a continuous segment obtained after segmenting the vessel. The vessel to be analyzed can be any type of vessel, such as the coronary artery, cerebral vessel, or aorta; the object to which the vessel belongs is not limited. The vascular input information for obtaining the vessel segment to be analyzed can be the input information used to analyze the vessel segment. This vascular input information can be externally input or determined based on the analysis results of a previous vessel segment.

[0052] In some embodiments, the vascular input information may include multiple information items, and the acquisition methods for different information items may vary. For example, the vascular input information may include blood flow information, input end velocity distribution coefficient, and vascular segment geometric parameters. Blood flow information may be the magnitude of blood flow in the vascular segment to be analyzed, i.e., the volumetric velocity of blood flow, which refers to the amount of blood flowing through a certain cross section of the vascular segment to be analyzed per unit time. For example, blood flow information may be the overall average blood flow of the vascular segment to be analyzed, or it may be the collection of blood flow at various points in the vascular segment to be analyzed. The velocity distribution coefficient may be a parameter reflecting the blood flow state at various points on a certain cross section of the vascular segment to be analyzed. The vascular segment geometric parameters refer to the geometric data at various locations of the vascular segment, such as the length, thickness, radius, and diameter of the vascular segment, or the vascular segment geometric parameters may include the coordinate values ​​at each location. The vascular segment geometric parameters may be obtained through methods such as angiography or CTA, for example, by acquiring a vascular image of the vascular segment to be analyzed and extracting the vascular segment geometric parameters based on the vascular image. The method of acquiring the vascular image is not limited in this regard.

[0053] Optionally, the blood flow information value can be a preset initial value, which can be set according to the boundary conditions of the blood vessel to be analyzed, or according to a preset fixed value, or a randomly generated value, or it can be obtained through iterative adjustment during the iterative analysis of the blood vessel segment to be analyzed. There are no limitations on this.

[0054] Optionally, the input velocity distribution coefficient can be either the initial velocity distribution coefficient or the output velocity distribution coefficient of the previous blood vessel segment to be analyzed. Specifically, when the blood vessel segment to be analyzed is the first segment of the blood vessel to be analyzed, the input velocity distribution coefficient of that segment can be the initial velocity distribution coefficient. This initial velocity distribution coefficient can be set based on the boundary conditions of the blood vessel to be analyzed.

[0055] Based on the above embodiments, the method for obtaining the vessel segment to be analyzed can be as follows: the vessel to be analyzed is segmented to obtain multiple vessel segments. The segmentation process can involve arbitrarily dividing the vessel into segments according to actual needs, for example, segmenting the vessel into fixed lengths of 0.1 cm. Correspondingly, the geometric parameters of the vessel segments to be analyzed can be the geometric parameters extracted from each segment based on the segmented image of the vessel to be analyzed.

[0056] In this embodiment, the blood vessel to be analyzed is segmented to obtain the blood vessel input information of each segment. This allows for more accurate calculation of energy loss in key areas (segments with narrowing, curvature, bifurcation, etc.). This solves the problem that when the energy loss of the entire blood vessel to be analyzed is calculated directly, the flow velocity distribution in the cross-section of the blood vessel changes in the key area, resulting in a significantly higher loss of blood flow mechanical energy downstream of the key area compared to normal blood vessels, which leads to large calculation errors and low accuracy in energy loss calculation.

[0057] S120. Input the blood vessel input information into the pre-trained flow velocity distribution coefficient prediction model to obtain the output flow velocity distribution coefficient of the blood vessel segment to be analyzed.

[0058] The pre-training of the prediction model can begin by defining the desired prediction model, setting an activation function for activation, obtaining the format used by the prediction model during operation, and then compiling and fitting the defined prediction model. Training the prediction model involves adjusting the model parameters using a training dataset, evaluating the prediction model based on validation data, and completing the training process if the evaluation is satisfactory. The activation function can be a regression function, a binary classification function, a multi-class classification function, etc., and this invention does not impose specific limitations.

[0059] Optionally, the flow velocity distribution coefficient prediction model can be of various types, including but not limited to logistic regression models, artificial neural network models, and decision tree models. The aforementioned flow velocity distribution coefficient prediction model, trained with sample data, processes the input information of the blood vessel, predicts the flow velocity distribution coefficient of the blood vessel segment to be analyzed, and obtains the output flow velocity distribution coefficient of the blood vessel segment to be analyzed.

[0060] The sample data used to regress the output velocity distribution coefficient of the blood vessel segment to be analyzed includes the blood vessel input information of the sample blood vessel segment and the standard output velocity distribution coefficient.

[0061] The vascular input information of the sample vascular segment and the flow velocity distribution coefficient at the standard output end can be obtained by performing a three-dimensional simulation of the sample vascular segment to obtain simulation parameters.

[0062] For example, after obtaining the input velocity distribution coefficient of the sample blood vessel segment, the following parameters can be regressed:

[0063] γ out =f γ (Geometric parameters, blood flow information q, γ) in )

[0064] Among them, regression methods include, but are not limited to, multiple linear regression, machine learning, etc. In this data fitting stage, blood flow information can be directly extracted from the velocity data of each point on the cross section in the three-dimensional blood flow simulation, and the geometric parameters of the blood vessel segment can be extracted from the three-dimensional simulation model. The input velocity distribution coefficient and the output velocity distribution coefficient can be calculated based on the three-dimensional blood flow simulation data, respectively.

[0065] By adjusting the undetermined parameters in the flow velocity distribution coefficient prediction model using sample data from various blood vessel segments, a trained flow velocity distribution coefficient prediction model is obtained. Thus, given the completed training of the flow velocity distribution coefficient prediction model, the output flow velocity distribution coefficient can be calculated based on the known input flow velocity distribution coefficient. It should be noted that the undetermined parameters in the flow velocity distribution coefficient prediction model can be determined according to the type of prediction model. For example, in the case of a neural network model, these undetermined parameters can be weights and biases from the neural network model.

[0066] In this embodiment, the output velocity distribution coefficient of the blood vessel segment to be analyzed is predicted by a pre-trained end-to-end velocity distribution coefficient prediction model, which simplifies the process of determining the output velocity distribution coefficient.

[0067] S130. Input the blood vessel input information and the output velocity distribution coefficient into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

[0068] The method for obtaining the mechanical energy loss prediction model can employ the regression method described above, which will not be elaborated further here. The mechanical energy loss value can be the difference between the mechanical energy at the input end and the mechanical energy at the output end of the blood vessel segment to be analyzed. In this embodiment, the type of mechanical energy loss prediction model is not limited. The sample data used to train the mechanical energy loss prediction model may include blood flow information of the sample blood vessel segment, geometric parameters of the blood vessel segment, velocity distribution coefficient at the input end and velocity distribution coefficient at the output end, and standard mechanical energy loss.

[0069] Geometric parameters of the blood vessel segment, blood flow information, velocity distribution coefficients at the input and output ends, and mechanical energy loss can be extracted from the three-dimensional blood flow simulation results or calculated based on the extracted data. Mechanical energy loss is then fitted using model regression.

[0070] ΔE=f e (Geometric parameters, blood flow information q, γ) in γ out )

[0071] By considering parameters such as vascular geometry and blood flow, and introducing a velocity distribution coefficient as a variable to describe the velocity distribution of the blood flow cross section, a new mechanical energy loss calculation model is obtained through regression and other methods, which further improves the accuracy of mechanical energy loss calculation in vascular segments.

[0072] Once the mechanical energy loss prediction model has been trained, the blood flow information, geometric parameters, input velocity distribution coefficient, and output velocity distribution coefficient of the vessel segment to be analyzed are processed by the model to predict the mechanical energy loss value of that segment. It should be noted that the vessel to be analyzed comprises multiple segments; therefore, the sum of the mechanical energy loss values ​​of all segments is the total mechanical energy loss value of the vessel to be analyzed.

[0073] In one optional embodiment, the method for obtaining the velocity distribution coefficient prediction model may include acquiring sample blood vessels, performing three-dimensional blood flow simulation on the sample blood vessels, segmenting the sample blood vessels, extracting the blood flow velocity distribution, blood flow information, and geometric parameters of each sample blood vessel segment at the inlet and outlet sections, determining the velocity distribution coefficient of each sample blood vessel segment at the inlet and outlet sections based on the blood flow velocity distribution of each sample blood vessel segment at the inlet and outlet sections, forming multiple sets of first sample data based on the velocity distribution coefficient, blood flow information, and geometric parameters of the blood vessel segment set at the inlet and outlet sections of each sample blood vessel segment, and obtaining the velocity distribution coefficient prediction model based on regression of the first sample data.

[0074] The blood flow velocities at the inlet and outlet sections can be the blood flow velocities at each point on the cross-section of the blood vessel segment to be analyzed, at the inlet and outlet ends, respectively.

[0075] The velocity distribution coefficient of the inlet and outlet sections can be calculated based on the blood flow velocities at the input and output ends using any of the following methods:

[0076] Calculation method 1:

[0077] Assume the velocity distribution along the cross-section of the blood vessel conforms to the following law:

[0078]

[0079] Where γ is the cross-sectional velocity distribution coefficient, r is the lumen radius at any point on the cross-section, R is the maximum lumen radius, U is the blood flow velocity at each point on the cross-section, and Umean is the average flow velocity of the cross-section. When obtaining the obtained three-dimensional blood flow simulation results, γ can be obtained by regression from the calculated results cross-section, or it can be obtained through other methods such as experiments. No specific limitation is made here.

[0080] Calculation Method 2:

[0081] Assume the flow velocity in the blood vessel is a piecewise distribution function:

[0082] U = R, 0 ≤ r ≤ R

[0083]

[0084] Where γ is the cross-sectional velocity distribution coefficient, α is the regression fitting coefficient, r is the radius of the cavity at any point on the cross-section, R is the maximum cavity radius, and U is the flow velocity. Both γ and α can be obtained through regression, and no specific limitations are made here.

[0085] Calculation method 3:

[0086] Consider the global slice velocity distribution across the cross section:

[0087]

[0088] This method can be obtained by integrating over the cross section, where γ is the cross section velocity distribution coefficient, r is the lumen radius at any point on the cross section, R is the maximum lumen radius, U is the blood flow velocity at each point on the cross section, and Umean is the average flow velocity of the cross section.

[0089] The velocity distribution coefficient can also be calculated using other methods, and this application does not impose any specific limitations.

[0090] By using any of the above calculation methods, the input velocity distribution coefficient and output velocity distribution coefficient of the blood vessel segment can be obtained by fitting the blood flow velocity of each segment cross section. The above data of each blood vessel segment form the first sample data to train the velocity distribution coefficient prediction model.

[0091] By introducing a velocity distribution coefficient as a variable to describe the velocity distribution across a blood flow cross-section, the impact of the velocity distribution at each point on the analyzed vessel segment's cross-section on subsequent energy loss can be clearly obtained, thereby further improving the calculation accuracy of energy loss in the analyzed vessel segment. The velocity distribution coefficient prediction model automates the calculation of this coefficient, which previously required significant manual labor, greatly saving manpower.

[0092] The method for obtaining the mechanical energy loss prediction model may include: determining the mechanical energy of the inlet and outlet based on the blood flow velocity distribution and blood flow information and blood flow pressure of the inlet and outlet sections, and obtaining the mechanical energy loss of each sample blood vessel segment; forming multiple sets of second sample data based on the flow velocity distribution coefficient, blood flow information, blood vessel segment geometric parameters and corresponding mechanical energy loss of each sample blood vessel segment; and obtaining the mechanical energy loss prediction model based on the regression of the second sample data.

[0093] The blood flow velocities at the inlet and outlet sections can be the blood flow velocities at each point on the cross section of the blood vessel segment to be analyzed, respectively, at the input and output ends. The velocity distribution coefficients at the input and output ends are obtained by calculating the velocity distribution coefficients at the inlet and outlet sections based on the blood flow velocities at the input and output ends, as described above. This will not be elaborated here. The geometric parameters of the blood vessel segment, blood flow information, blood flow pressure, and mechanical energy loss can be extracted from the three-dimensional blood flow simulation results or calculated based on the extracted data.

[0094] In the above embodiments, a flow velocity distribution coefficient prediction model is obtained based on the first sample data through regression, and a mechanical energy loss prediction model is obtained based on the second sample data. The specific regression method for the prediction models is not limited here. The corresponding regression method can be determined according to the model type of the prediction model. For example, when the prediction model is a neural network model, iterative training can be used to iteratively adjust the network parameters in the prediction model based on the sample data until a fully trained prediction model is obtained.

[0095] By introducing parameters such as flow velocity distribution coefficient, blood flow information, and vascular segment geometric parameters to train the prediction model, the accuracy of the prediction model is effectively improved, thereby enhancing the precision of calculating the mechanical energy loss value of the vascular segment.

[0096] The technical solution of this embodiment obtains the vascular input information of the vascular segment to be analyzed, inputs the vascular input information into a pre-trained velocity distribution coefficient prediction model to obtain the output velocity distribution coefficient of the vascular segment to be analyzed, and inputs the mechanical energy loss prediction model based on the vascular input information and the output velocity distribution coefficient to obtain the mechanical energy loss value of the vascular segment to be analyzed. Based on the vascular input information and the output velocity distribution coefficient, the mechanical energy loss value of the vascular segment to be analyzed is calculated. This solves the problem that the existing mechanical energy loss calculation formula cannot reflect the mechanical energy loss at the downstream end, has low accuracy, and has large calculation errors, thereby improving the accuracy of energy loss calculation.

[0097] Example 2

[0098] Figure 2 This is a flowchart of a method for determining blood flow mechanical energy loss according to Embodiment 2 of the present invention. This embodiment is an optimization based on Embodiment 1 described above. Figure 2 As shown, the method includes:

[0099] S210. Obtain the input and output pressures of the blood vessel segment to be analyzed.

[0100] Among them, blood flow information can be the magnitude of blood flow in the blood vessel segment to be analyzed, that is, the volumetric velocity of blood flow, which refers to the amount of blood flowing through a certain cross section of the blood vessel segment to be analyzed per unit time. For example, blood flow information can be the overall average blood flow of the blood vessel segment to be analyzed, or it can be the collection of blood flow at various points in the blood vessel segment to be analyzed.

[0101] The input and output pressures can be determined based on the blood flow pattern of the blood vessel segment being analyzed. For example, if the segment is an artery with blood flow from the proximal to the distal end, the input end of that segment is the proximal end; conversely, if the segment is a vein, the input end is the distal end. Optionally, the input and output pressures can be preset initial values. These initial values ​​can be set based on the boundary conditions of the segment, or they can be preset fixed values, randomly generated values, or they can be obtained through iterative adjustments during the iterative analysis of the segment. No limitation is imposed on these settings.

[0102] Optionally, the input and output pressures of the blood vessel segment to be analyzed can be values ​​between the measured diastolic and systolic pressure ranges. The input and output pressure values ​​at different locations of the blood vessel segment to be analyzed can be different, and this invention does not impose specific limitations.

[0103] S220. The blood vessel to be analyzed is segmented to obtain multiple blood vessel segments to be analyzed. For each blood vessel segment to be analyzed, the blood vessel input information of the blood vessel segment to be analyzed is obtained.

[0104] The vascular input information of the vascular segment to be analyzed may include the vascular segment's geometric parameters, blood flow information, and the velocity distribution coefficient at the input end. The blood flow information may be the magnitude of the blood flow in the vascular segment to be analyzed, i.e., the volumetric velocity of blood flow, which refers to the amount of blood flowing through a certain cross section of the vascular segment to be analyzed per unit time. For example, the blood flow information may be the overall average blood flow of the vascular segment to be analyzed, or it may be the collection of blood flow at various points in the vascular segment to be analyzed.

[0105] S230. Input the blood vessel input information into the pre-trained flow velocity distribution coefficient prediction model to obtain the output flow velocity distribution coefficient of the blood vessel segment to be analyzed.

[0106] S240. Input the blood vessel input information and the output velocity distribution coefficient into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

[0107] The sum of the mechanical energy loss values ​​of all the blood vessel segments to be analyzed is the mechanical energy loss value of the blood vessel to be analyzed.

[0108] S250. Energy conservation verification is performed based on the blood flow information, velocity distribution coefficient, input pressure, output pressure, and mechanical energy loss value of the blood vessel segment to be analyzed.

[0109] The mechanical energy loss value of the blood vessel segment to be analyzed can be the sum of the mechanical energy loss values ​​of all blood vessel segments to be analyzed.

[0110] The energy conservation verification can be based on comparing the energy difference between the input and output mechanical energies with the mechanical energy loss value of the blood vessel segment to be analyzed to determine whether energy conservation is satisfied. Optionally, the mechanical energy at the input end of the blood vessel segment to be analyzed can include blood flow energy and input static pressure energy, and the mechanical energy at the output end of the blood vessel segment to be analyzed can include blood flow energy and output static pressure energy. Blood flow energy can be determined based on blood flow information, and input / output static pressure energy can be determined based on input / output pressure. The mechanical energy can be determined by the blood flow energy determined by the input pressure (input static pressure energy), blood flow information, and input velocity distribution coefficient, and the output mechanical energy can be determined by the blood flow energy determined by the output pressure (output static pressure energy), blood flow information, and output velocity distribution coefficient. Specifically, the blood velocity distribution can be determined according to the velocity distribution coefficient (input velocity distribution coefficient or output velocity distribution coefficient), for example, through the relationship formula between blood velocity distribution and velocity distribution coefficient provided in the above embodiments. At the input end, the kinetic energy of the blood flow is determined using the kinetic energy calculation formula based on the blood flow information and velocity distribution at the input end. At the output end, the kinetic energy of the blood flow is determined using the same formula based on the blood flow information and velocity distribution at the output end. At either end, the sum of the static pressure energy and the kinetic energy of the blood flow constitutes the mechanical energy.

[0111] The hypothetical values ​​of the given blood flow information and the hypothetical values ​​of the input / output pressure are input into the velocity distribution coefficient prediction model and the mechanical energy loss prediction model. Based on the law of conservation of energy, an energy conservation equation is constructed to verify the energy conservation.

[0112] Optionally, an error threshold can be set for energy conservation verification. If the energy difference between the input and output mechanical energy of the analyzed blood vessel is within the preset error threshold when performing energy conservation verification, the comparison result with the mechanical energy loss value of the blood vessel segment to be analyzed is within the preset error threshold. Otherwise, the energy conservation verification is determined to have failed.

[0113] If the energy conservation verification is successful, then S270 is executed; if the energy conservation verification fails, then S260 is executed.

[0114] S260. Update the vascular input information of the vascular segment to be analyzed, and redetermine the mechanical energy loss value of the vascular segment to be analyzed based on the updated vascular input information.

[0115] Optionally, theoretically, if the blood flow information and input / output pressure of the blood vessel segment to be analyzed are true solutions, then the mechanical energy in the law of conservation of energy should be conserved. However, the blood flow information and input / output pressure of the blood vessel segment to be analyzed are currently conjectured values. Therefore, the current energy conservation equation is not conserved, and the energy difference between the mechanical energy at the input end and the mechanical energy at the output end of the blood vessel segment to be analyzed is an unbalanced state. It is necessary to update the blood flow information and input / output pressure of the blood vessel segment to be analyzed.

[0116] The process of updating the vascular input information of the vessel segment to be analyzed can be achieved by comparing the energy difference between the mechanical energy at the input end and the mechanical energy at the output end of the vessel segment with the mechanical energy loss value of the vessel segment. This comparison determines the adjustment direction and / or adjustment value of the vascular input information, and updates the vascular input information based on the adjustment direction and / or adjustment value. Optionally, the adjustment direction of the vascular input information can be increased or decreased, and the adjustment value can be a fixed value determined according to the actual situation; this invention does not impose specific limitations. Preferably, the updated vascular input information can be applied to all points on the cross-section of the vessel segment to be analyzed.

[0117] Specifically, after redetermining the mechanical energy loss value of the blood vessel segment to be analyzed, an energy conservation verification is performed on the redetermined mechanical energy loss value of the blood vessel segment to be analyzed. If the energy conservation verification of the redetermined mechanical energy loss value of the blood vessel segment to be analyzed is successful, then S270 is executed. If the energy conservation verification of the redetermined mechanical energy loss value of the blood vessel segment to be analyzed fails, then S250 is executed again until the redetermined mechanical energy loss value of the blood vessel segment to be analyzed passes the energy conservation verification.

[0118] S270, Output the mechanical energy loss value of the blood vessel segment to be analyzed.

[0119] The technical solution of this embodiment obtains the vascular input information of the blood vessel segment to be analyzed, inputs the vascular input information into a pre-trained velocity distribution coefficient prediction model to obtain the output velocity distribution coefficient of the blood vessel segment to be analyzed, and inputs the mechanical energy loss prediction model based on the vascular input information and the output velocity distribution coefficient to obtain the mechanical energy loss value of the blood vessel segment to be analyzed. Based on the vascular input information and the output velocity distribution coefficient, the mechanical energy loss value of the blood vessel segment to be analyzed is calculated. This solves the problem that the existing mechanical energy loss calculation formula cannot reflect the mechanical energy loss at the downstream end, has low accuracy, and large calculation error, thus improving the accuracy of energy loss calculation. At the same time, by verifying the energy conservation of the mechanical energy loss value, the parameters in the prediction model are updated if the energy conservation verification fails, further ensuring the high accuracy of energy loss calculation.

[0120] Example 3

[0121] Figure 3 This is a schematic diagram of a blood flow mechanical energy loss determination device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0122] The input information acquisition module 310 is used to acquire the vascular input information of the vascular segment to be analyzed.

[0123] The flow velocity distribution coefficient calculation module 320 is used to input the blood vessel input information into a pre-trained flow velocity distribution coefficient prediction model to obtain the output flow velocity distribution coefficient of the blood vessel segment to be analyzed.

[0124] The mechanical energy loss calculation module 330 is used to input the blood vessel input information and the output end flow velocity distribution coefficient into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

[0125] Optionally, the blood flow mechanical energy loss determining device further includes:

[0126] The segmentation module is used to segment the blood vessel to be analyzed to obtain multiple blood vessel segments to be analyzed. The blood vessel input information of the blood vessel segment to be analyzed includes blood flow information, input end velocity distribution coefficient and blood vessel segment geometric parameters. The input end velocity distribution coefficient is the initial velocity distribution coefficient or the output end velocity distribution coefficient of the previous blood vessel segment to be analyzed.

[0127] Correspondingly, the sum of the mechanical energy loss values ​​of all the blood vessel segments to be analyzed is the mechanical energy loss value of the blood vessel to be analyzed.

[0128] Optionally, the blood flow mechanical energy loss determining device further includes:

[0129] The pressure acquisition module is used to acquire the input and output pressures of the blood vessel segment to be analyzed.

[0130] The energy conservation verification module is used to perform energy conservation verification based on the blood flow information, velocity distribution coefficient, input pressure, output pressure, and mechanical energy loss value of the blood vessel segment to be analyzed.

[0131] The vascular input information update module is used to update the vascular input information of the vascular segment to be analyzed if the energy conservation verification fails, and to redetermine the mechanical energy loss value of the vascular segment to be analyzed based on the updated vascular input information.

[0132] Optionally, the energy conservation verification module includes:

[0133] The blood flow energy calculation unit is used to determine blood flow energy based on blood flow information and velocity distribution coefficient.

[0134] The mechanical energy calculation unit is used to determine the input mechanical energy based on the input pressure and the blood flow energy, and to determine the output mechanical energy based on the output pressure and the blood flow energy.

[0135] The energy conservation judgment unit compares the energy difference between the input mechanical energy and the output mechanical energy with the mechanical energy loss value of the blood vessel segment to be analyzed to determine whether energy conservation is satisfied.

[0136] Optionally, the vascular input information update module is specifically used for:

[0137] Based on the comparison between the energy difference and the mechanical energy loss value of the blood vessel segment to be analyzed, the adjustment direction and / or adjustment value of the blood vessel input information are determined, and the blood vessel input information of the blood vessel segment to be analyzed is updated based on the adjustment direction and / or adjustment value.

[0138] Optionally, the blood flow mechanical energy loss determination device further includes: a flow velocity distribution coefficient prediction model training module and a mechanical energy loss prediction model training module.

[0139] Optionally, the velocity distribution coefficient prediction model training module includes:

[0140] The three-dimensional blood flow simulation unit is used to acquire sample blood vessels and perform three-dimensional blood flow simulation on the sample blood vessels; the sample blood vessels are segmented and the blood flow velocity distribution, blood flow information and geometric parameters of each sample blood vessel segment at the inlet and outlet sections are extracted;

[0141] The first sample data generation unit is used to determine the flow velocity distribution coefficient of each sample blood vessel segment at the inlet and outlet sections based on the blood flow velocity distribution of each sample blood vessel segment at the inlet and outlet sections, and to form multiple sets of first sample data based on the flow velocity distribution coefficient of each sample blood vessel segment at the inlet and outlet sections, blood flow information and the geometric parameters of the blood vessel segment set.

[0142] The distribution coefficient prediction model training unit is used to obtain the flow velocity distribution coefficient prediction model based on the regression of the first sample data.

[0143] Optionally, the mechanical energy loss prediction model training module includes:

[0144] The sample mechanical energy calculation unit is used to determine the inlet and outlet mechanical energy based on the blood flow velocity distribution, blood flow pressure and blood flow information of the inlet and outlet sections, and to obtain the mechanical energy loss of each sample blood vessel segment.

[0145] The second sample data generation unit is used to generate multiple sets of second sample data based on the flow velocity distribution coefficient, blood flow information, geometric parameters of the blood vessel segment, and corresponding mechanical energy loss of each sample blood vessel segment.

[0146] The mechanical energy loss prediction model training unit obtains the mechanical energy loss prediction model based on the regression of the second sample data.

[0147] The blood flow mechanical energy loss determination device provided in the embodiments of the present invention can execute the blood flow mechanical energy loss determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0148] Example 4

[0149] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0150] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0151] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0152] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining blood flow mechanical energy loss.

[0153] In some embodiments, the method for determining blood flow mechanical energy loss may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining blood flow mechanical energy loss described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining blood flow mechanical energy loss by any other suitable means (e.g., by means of firmware).

[0154] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0155] Computer programs for implementing the blood flow mechanical energy loss determination method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0156] Example 5

[0157] Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a method for determining blood flow mechanical energy loss, the method comprising:

[0158] For the blood vessel segment to be analyzed, obtain the blood vessel input information of the blood vessel segment to be analyzed;

[0159] The blood vessel input information is input into a pre-trained flow velocity distribution coefficient prediction model to obtain the output flow velocity distribution coefficient of the blood vessel segment to be analyzed.

[0160] The blood vessel input information and the output velocity distribution coefficient are input into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

[0161] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0162] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0163] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0164] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0165] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0166] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining blood flow mechanical energy loss, characterized in that, include: For the blood vessel segment to be analyzed, the vascular input information of the blood vessel segment to be analyzed is obtained; the vascular input information of the blood vessel segment to be analyzed includes blood flow information, input end velocity distribution coefficient and vascular segment geometric parameters; The vascular input information is input into a pre-trained flow velocity distribution coefficient prediction model to obtain the output flow velocity distribution coefficient of the vascular segment to be analyzed; the flow velocity distribution coefficient is a variable describing the flow velocity distribution of the blood flow cross section. The blood vessel input information and the output flow velocity distribution coefficient are input into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

2. The method according to claim 1, characterized in that, The method further includes: The blood vessel to be analyzed is segmented to obtain multiple blood vessel segments to be analyzed. The input velocity distribution coefficient is the initial velocity distribution coefficient or the output velocity distribution coefficient of the previous blood vessel segment to be analyzed. Correspondingly, the sum of the mechanical energy loss values ​​of all the blood vessel segments to be analyzed is the mechanical energy loss value of the blood vessel to be analyzed.

3. The method according to claim 1, characterized in that, The method further includes: Obtain the input and output pressures of the blood vessel segment to be analyzed; Energy conservation verification is performed based on the blood flow information, velocity distribution coefficient, input pressure, output pressure, and mechanical energy loss value of the blood vessel segment to be analyzed. If the energy conservation verification fails, the vascular input information of the blood vessel segment to be analyzed is updated, and the mechanical energy loss value of the blood vessel segment to be analyzed is re-determined based on the updated vascular input information.

4. The method according to claim 3, characterized in that, The energy conservation verification based on the blood flow information, flow velocity distribution coefficient, input pressure, output pressure, and mechanical energy loss value of the blood vessel segment to be analyzed includes: Blood flow energy is determined based on the blood flow information and velocity distribution coefficient. The input mechanical energy is determined based on the input pressure and the blood flow energy, and the output mechanical energy is determined based on the output pressure and the blood flow energy. Based on the energy difference between the input mechanical energy and the output mechanical energy, the energy loss value of the blood vessel segment to be analyzed is compared with the energy loss value to determine whether energy conservation is satisfied.

5. The method according to claim 4, characterized in that, The updating of the vascular input information of the vascular segment to be analyzed includes: Based on the comparison between the energy difference and the mechanical energy loss value of the blood vessel segment to be analyzed, the adjustment direction and / or adjustment value of the blood vessel input information are determined, and the blood vessel input information of the blood vessel segment to be analyzed is updated based on the adjustment direction and / or adjustment value.

6. The method according to claim 1, characterized in that, The method for obtaining the velocity distribution coefficient prediction model includes: A sample blood vessel is acquired, and a three-dimensional blood flow simulation is performed on the sample blood vessel. The sample blood vessel is segmented, and the blood flow velocity distribution, blood flow information, and geometric parameters of each blood vessel segment at the inlet and outlet sections are extracted. Based on the blood flow velocity distribution of each sample blood vessel segment at the inlet and outlet sections, the velocity distribution coefficient of each sample blood vessel segment at the inlet and outlet sections is determined. Based on the velocity distribution coefficient of each sample blood vessel segment at the inlet and outlet sections, blood flow information, and geometric parameters of the blood vessel segment, multiple sets of first sample data are formed. A flow velocity distribution coefficient prediction model is obtained based on the regression of the first sample data.

7. The method according to claim 6, characterized in that, The mechanical energy loss prediction model includes: Based on the blood flow velocity distribution and blood flow information and blood flow pressure at the inlet and outlet sections, the mechanical energy at the inlet and outlet sections was determined, and the mechanical energy loss of each sample blood vessel segment was obtained. Multiple sets of second sample data are generated based on the flow velocity distribution coefficient, blood flow information, vascular segment geometric parameters, and corresponding mechanical energy loss of each sample vascular segment at the inlet and outlet cross sections. A mechanical energy loss prediction model is obtained based on the regression of the second sample data.

8. A device for determining blood flow mechanical energy loss, characterized in that, include: The input information acquisition module is used to acquire the vascular input information of the vascular segment to be analyzed; the vascular input information of the vascular segment to be analyzed includes blood flow information, input end flow velocity distribution coefficient and vascular segment geometric parameters; The velocity distribution coefficient calculation module is used to input the blood vessel input information into a pre-trained velocity distribution coefficient prediction model to obtain the output velocity distribution coefficient of the blood vessel segment to be analyzed; the velocity distribution coefficient is a variable describing the velocity distribution of the blood flow cross section. The mechanical energy loss calculation module is used to input the blood vessel input information and the output end flow velocity distribution coefficient into the mechanical energy loss prediction model to obtain the mechanical energy loss value of the blood vessel segment to be analyzed.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the blood flow mechanical energy loss determination method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining blood flow mechanical energy loss as described in any one of claims 1-7.

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