Total lung periodic respiration simulation method, device and equipment

Abstract

The invention relates to a whole-lung periodic respiration simulation method, device and equipment. The method comprises the following steps: constructing a plurality of first stages by scanning lung data, constructing a plurality of second stages by dissecting the lung data, forming a multi-stage airway model, and forming a lung alveolar model at the tail end of the second stage of the airway model; performing grid division on the lung acinus model and the inner flow field of the airway model to generate an airway inner flow field and a lung acinus discrete model; vertical negative pressure is applied to the pulmonary alveolar discrete model by applying cosine negative pressure to the surface of the solid; in the direction from the second stage close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, recording the fluid velocity at the specified stage in each section of the airway model in a segmented manner, and performing internal flow field motion simulation on the previous section of the section in the airway model when the fluid is transmitted to the specified stage of the previous section of the section in the airway model; and monitoring lung alveolar deformation to obtain lung alveolar physical field data, performing internal flow field motion monitoring on each segment to obtain internal flow field respiration data, and generating a whole-lung periodic respiration simulation result.

Description

Technical Field

[0001] The present disclosure relates to the technical field of human lung breathing simulation, and in particular to a whole-lung periodic breathing simulation method, device and equipment. Background Art

[0002] The lungs are important organs for maintaining human vital signs. There are many types of lung diseases with complex causes. In order to gain a deeper understanding of the causes of respiratory diseases, scientific research urgently needs to have a clearer understanding of the movement patterns of fluids in the respiratory system and the deformation of pulmonary alveoli. Computational fluid dynamics is currently the most widely used method to predict the flow field of the lungs. However, due to the complexity of the lung model, the simulation of the entire lung will take up a lot of computing resources. Therefore, the current whole lung simulation is mostly a simplified model that ignores many details. For example, the whole lung model proposed by April Si X et al. in SARS COV-2 virus-laden droplets coughed from deep lungs: Numerical quantification in a single-path whole respiratory tract geometry [J]. Physics of Fluids, 2021, 33 (2) consists of only a single path. The fluid signal is input with a given boundary condition cosine to simulate lung breathing. However, this positive pressure ventilation breathing method does not conform to the fact that the diaphragm, scalene muscles, intercostal muscles and other respiratory-related muscle groups generate negative pressure to guide the deformation of pulmonary alveoli, thereby generating flow.

[0003] For example, Chen L et al. proposed to apply pressure only to the pulmonary alveoli and simulate the deformation of the flow field in the pulmonary alveoli by fluid-structure interaction in idiopathic interstitialpneumonias[J].PLOS ONE, 2019, 14(3): e0214441. For another example, Poorbahrami K et al. proposed to use the real model that can be scanned by enhanced CT for simulation in Regional flow anddeposition variability in adult female lungs: A numerical simulation pilot study[J]. Clinical Biomechanics, 2019, 66: 40-49.

[0004] However, the local simulation verification method can only verify the similarity of data trends, which is quite different from real lung breathing. Therefore, the accuracy verification is not convincing. In addition, the boundary conditions are different from the real breathing pattern and cannot simulate the situation of a single lung lobe. Summary of the invention

[0005] In order to overcome the technical problems existing in the related technology, such as large differences from real lung breathing, which makes the accuracy verification unconvincing, and the boundary conditions are different from the real breathing pattern, and the situation of a single lung lobe cannot be simulated, the present disclosure provides a whole-lung periodic breathing simulation method, device and equipment, which aims to simulate the whole-lung fluid movement mode under the condition of real pulmonary alveoli under negative pressure breathing mode, so as to improve accuracy and credibility.

[0006] According to a first aspect of an embodiment of the present disclosure, a whole-lung periodic breathing simulation method is provided, comprising: constructing a plurality of first levels by scanning lung data, constructing a plurality of second levels by adding random factors to anatomical lung data to generate an asymmetric structure, forming a multi-level airway model constructed by the plurality of first levels and the plurality of second levels, and forming a lung alveolar model by clustering a plurality of spheres of preset diameters at the end of the second level of the airway model, wherein the plurality of the second levels are divided into a plurality of segments;

[0007] Meshing the internal flow field of the airway model to generate the internal flow field of the airway and meshing the pulmonary alveolar model to divide the fluid and solid in the pulmonary alveolar model to generate a pulmonary alveolar discrete model;

[0008] Establishing a bidirectional coupling data transmission between a pulmonary alveolar discrete model and a solid surface of the pulmonary alveolar model, and applying a vertical negative pressure to the outer surface of the solid region to drive the solid region to move, and transmitting the pressure to the fluid region through the boundary layer, so that the fluid region is subjected to a force to generate fluid motion, so as to simulate the deformation of the pulmonary alveoli by the negative pressure and to make the segmented internal flow field close to the pulmonary alveolar model in the airway flow field generate motion driven by the deformation of the pulmonary alveoli;

[0009] The fluid velocity at the designated level of each segment of the airway model is sequentially recorded in segments from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and the fluid is transferred to the designated level of the previous segment of the segment in the airway model according to the velocity boundary condition, and the previous segment of the segment in the airway model is simulated for internal flow field motion, and the deformation of the last stage of the second level of the airway model is monitored to obtain pulmonary alveolar physical field data and the internal flow field motion of each segment is monitored to obtain internal flow field breathing data;

[0010] A whole-lung periodic breathing simulation result is generated according to the fluid velocity at a specified level in each of the segments, the lung acinus physical field data and the internal flow field breathing data.

[0011] In a possible implementation, multiple first levels are constructed by scanning lung data, and multiple second levels are constructed by adding random factors to anatomical lung data to generate asymmetric structures, so as to form a multi-level airway model constructed by multiple first levels and multiple second levels, and a lung alveolar model is formed by clustering multiple spheres with preset diameters at the end of the second level of the airway model, including:

[0012] Constructing multiple first levels G0-G6 with the scanned lung data;

[0013] Adding random factors to the anatomical lung data to generate an asymmetric structure to construct multiple second levels of G6-G23, forming a multi-level airway model constructed of 7 first levels and 17 second levels;

[0014] At the end of the airway model G23, the pulmonary alveolar model is formed by clustering a plurality of spheres of the preset diameter;

[0015] Among them, multiple second levels of G6-G23 are divided into segments G6-G13, G13-G20, and G20-G23.

[0016] In a possible implementation, the fluid velocity at a specified level in each of the segments of the airway model is sequentially recorded in segments from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and the fluid is transferred to the specified level of the previous segment of the segment in the airway model according to the velocity boundary condition, and the previous segment of the segment in the airway model is simulated for internal flow field motion, and the deformation of the second stage of the airway model is monitored at the end to obtain pulmonary alveolar physical field data and monitor the internal flow field motion of each segment to obtain internal flow field breathing data, including:

[0017] The velocity of the fluid at G20 of the G20-G23 segment in the airway model is recorded, and the fluid is transferred to G20 of the G13-G20 segment in the airway model according to the velocity boundary condition, and the internal flow field motion simulation of the G13-G20 segment is performed, and the deformation of the second stage of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G13-G20 segment is monitored to obtain the internal flow field breathing data of the G13-G20 segment;

[0018] The velocity of the fluid at G13 of the G13-G20 segment in the airway model is recorded, and the fluid is transferred to G13 of the G6-G13 segment in the airway model according to the velocity boundary condition, and the internal flow field motion simulation of the G6-G13 segment is performed, and the deformation of the second stage of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G6-G13 segment is monitored to obtain the internal flow field respiratory data of the G6-G13 segment;

[0019] The fluid velocity at G6 of the G6-G13 segment in the airway model is recorded, and the fluid is transferred to G6 of the G0-G6 segment in the airway model according to the velocity boundary condition, and the internal flow field motion of the G0-G6 segment is simulated, and the deformation of the second-most stage of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G0-G6 segment is monitored to obtain the internal flow field respiratory data of the G0-G6 segment.

[0020] In one possible implementation, the preset diameter is 0.25 mm, and the preset number is 25.

[0021] In a possible implementation, generating a whole lung periodic breathing simulation result according to the fluid velocity at a specified level in each segment, the pulmonary alveolar physical field data, and the internal flow field breathing data includes:

[0022] According to whether the tidal volume and residual volume in the internal flow field respiratory data are respectively within the preset value range, the lung function index verification simulation result is obtained, wherein the preset value range corresponding to the tidal volume is 400-600ml, and the preset value range corresponding to the residual volume is 2000-2400ml;

[0023] The simulation results are verified according to the lung function index, and the whole lung periodic breathing simulation results are generated.

[0024] In a possible implementation, the Reynolds number of the internal flow field in the first stage of the airway model is greater than a preset turbulence threshold, and the Reynolds number of the internal flow field in the second stage of the airway model is less than the preset turbulence threshold;

[0025] The first stage of the airway model adopts a low Reynolds number corrected SST k-ω viscosity model, and the second stage of the airway model adopts a laminar viscosity model;

[0026] The control equation of the low Reynolds number corrected SST k-ω viscosity model is as follows:

[0027]

[0028] in, β=β0 f β ;

[0029] α k =α ω =0.5;

[0030] Among them, S ij is the average velocity strain rate tensor, δ ij is the Kronecker operator, ρ is the fluid density, ν T is the turbulent viscosity, u i ,u j (i, j = 1, 2, 3) represent the velocities in the x, y, and z directions respectively, k is the turbulent kinetic energy, and ω is the specific dissipation rate.

[0031] In a possible implementation, the discriminant formula of the cosine negative pressure P is:

[0032]

[0033] Among them, t is time and π is a mathematical constant.

[0034] In a possible implementation, the pulmonary alveolar physical field data includes at least one of the following: pulmonary alveolar volume change, pulmonary alveolar stress and strain distribution, and pulmonary alveolar discrete model motion mode.

[0035] According to a second aspect of an embodiment of the present disclosure, a whole-lung periodic breathing simulation device is provided, comprising: a construction module, configured to construct a plurality of first stages with scanned lung data, to construct a plurality of second stages with asymmetric structures generated by adding random factors to anatomical lung data, to form a multi-stage airway model constructed by a plurality of first stages and a plurality of second stages, and to form a lung alveolar model at the end of the second stage of the airway model by clustering a plurality of spheres of preset diameters, wherein the plurality of second stages are divided into a plurality of segments;

[0036] A flow field generation module is configured to mesh the internal flow field of the airway model to generate the internal flow field of the airway and mesh the pulmonary alveolar model to respectively divide the fluid and solid in the pulmonary alveolar model to generate a pulmonary alveolar discrete model;

[0037] The pressure module is configured to establish a bidirectional coupling data transmission between the pulmonary alveolar discrete model and the solid surface of the pulmonary alveolar model, and to drive the solid area by applying a vertical negative pressure to the outer surface of the solid area.

[0038] motion, transmitted to the fluid region through the boundary layer, so that the fluid region is subjected to force and fluid motion occurs, so as to simulate the deformation of the pulmonary alveoli caused by negative pressure and to cause the segmented internal flow field close to the pulmonary alveolar model in the airway flow field to move under the driving force of the pulmonary alveolar deformation;

[0039] The simulation and recording module is configured to sequentially record the fluid velocity at a specified level in each of the segments of the airway model from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and according to the velocity boundary condition, transfer the fluid to the specified level of the previous segment of the segment in the airway model, simulate the internal flow field motion of the previous segment of the segment in the airway model, and monitor the deformation of the second-stage end of the airway model to obtain pulmonary alveolar physical field data and monitor the internal flow field motion of each segment to obtain internal flow field breathing data;

[0040] The result generating module is configured to generate a whole lung periodic breathing simulation result according to the fluid velocity at a specified level in each of the segments, the lung acinus physical field data and the internal flow field breathing data.

[0041] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0042] a processor; a memory for storing instructions executable by the processor;

[0043] The processor is configured to execute the executable instructions stored in the memory to implement any method described in the first aspect.

[0044] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: by adding random factors to construct an asymmetric random whole lung model, which is closer to the actual situation, and then by bidirectionally coupling the data transmission between the flow field and the solid in the pulmonary alveolar area, the flow field motion mode and the deformation of the pulmonary alveoli in the actual situation are simulated, and then the flow field data is transmitted from bottom to top to the airway model G0 according to the velocity boundary conditions. In this way, the negative pressure ventilation method is adopted, which is the same as the actual breathing situation, to avoid the situation that the individual pulmonary alveoli are deformed and the airflow is inconsistent with the reality during the positive pressure ventilation simulation. By adopting the bottom-up segmented whole lung simulation method, the anatomical parameters and boundary condition transmission positions of the corresponding lung lobes can be selected, and a single lung lobe can be simulated. In the case of the real pulmonary alveoli under the negative pressure breathing mode, the whole lung fluid movement mode can be simulated, thereby improving the accuracy and credibility of the lung breathing simulation and the verification of the simulation results. At the same time, it can achieve the reduction of the calculation time in the whole lung simulation.

[0045] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0047] Figure 1 It is a flowchart of a whole-lung periodic breathing simulation method according to an exemplary embodiment.

[0048] Figure 2 is a schematic diagram of a G0-G6 airway model stage according to an exemplary embodiment.

[0049] Figure 3 is a schematic diagram of a G6-G13 airway model stage according to an exemplary embodiment.

[0050] Figure 4 It is a schematic diagram of an airway model of the G13-G20 stage according to an exemplary embodiment.

[0051] Figure 5 It is a schematic diagram of a G20-G23 stage airway model with pulmonary alveoli according to an exemplary embodiment.

[0052] Figure 6 It is a schematic structural diagram of a whole-lung periodic breathing simulation device according to an exemplary embodiment. DETAILED DESCRIPTION

[0053] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0054] Figure 1 FIG. 1 is a flow chart of a whole-lung periodic breathing simulation method according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0055] In step S11, multiple first levels are constructed using scanned lung data, and multiple second levels are constructed by adding random factors to anatomical lung data to generate asymmetric structures, thereby forming a multi-level airway model constructed by multiple first levels and multiple second levels, and a lung alveolar model is formed at the end of the second level of the airway model through multiple spherical clusters of preset diameters.

[0056] wherein a plurality of said second levels are divided into a plurality of segments;

[0057] The velocity boundary condition is the area average velocity, the cross-sectional velocity at the velocity inlet is a constant, and an overlapping area is added at the segment of the airway model to fully develop the velocity in the tube.

[0058] Steady-state simulation is applicable to situations where the physical quantities in the flow field are independent of time. When the physical quantities vary with time, non-steady-state simulation should be used. The simulation method mentioned in this patent uses the time-cosine-varying negative pressure of the pulmonary alveolar model. The key parameters such as velocity, pressure, and shear stress in the flow field are all time-dependent variables, so non-steady-state simulation is used.

[0059] The airway model and the pulmonary alveolar model are both located in a normal human body, so the airflow temperature can be considered as a constant temperature of 37°C, that is, the fluid density is 1.225 kg / m 3 , the viscosity is 1.7894×10 -5 kg(m·s).

[0060] In the specific implementation, the main airway branches, such as the main bronchus, left and right bronchi, etc., are identified based on the CT images, which constitute the first-level airway. For example, the main bronchus is accurately modeled as a wide passage extending from the throat to the lungs, and the left and right bronchi extend to the left and right sides respectively.

[0061] Add details to each primary airway branch to create secondary airways. These secondary airways simulate real asymmetric structures by adding random factors (random factors are related to the angle size and diameter of the offspring in the branch, and the angle between the parent and offspring in the branch). For example, on a branch of the left bronchus, the airway is slightly bent to the lower left, and the branch diameter is adjusted so that it is not completely symmetrical with the branch on the other side. This treatment ensures that the diameter of each airway end is different, increasing the realism of the model.

[0062] Segmentation and diameter adjustment: To further refine the model, the secondary airways are divided into multiple segments, and the diameter of each segment is adjusted according to real data or simulation requirements. For example, the diameter of the airways near the end of the lungs gradually decreases to reflect the natural structure of the small airways in the lungs.

[0063] After the airway model is built, a pulmonary alveolus model is added at the end of the second level of the airway model. These pulmonary alveoli are clustered by multiple spherical structures with preset diameters, simulating the basic functional units of the lungs.

[0064] Pulmonary alveolar model construction: At the end of each second-level airway, the same number of spherical structures are generated. These spherical structures are closely arranged to form a clustering effect of pulmonary alveoli.

[0065] In step S12, meshing the internal flow field of the airway model to generate the internal flow field of the airway and meshing the pulmonary alveolar model to divide the fluid and solid in the pulmonary alveolar model to generate a pulmonary alveolar discrete model;

[0066] The model of the pulmonary alveoli is divided into an outer shell and an inner flow field. The outer shell corresponds to solid simulation, and the inner flow field corresponds to fluid simulation. The fluid and solid are divided and executed separately, for example, two software can be used to calculate separately, and the data is transmitted to the other software at each iteration step.

[0067] In the disclosed embodiment, in order to perform fluid dynamics analysis, the entire airway model and the pulmonary alveolar model are meshed. For the airway model, the mesh is designed to accurately capture the flow of air in the airway; and for the pulmonary alveolar model, since the alveoli in the real lungs are separated and not a group, but most of them are close to each other, in the actual model embodiment, the relationship between the alveoli is simplified, and a hollow model composed of multiple balls is used to simulate the interaction between the alveolar solid and the internal flow field.

[0068] In step S13, a bidirectional coupling data transmission is established between the pulmonary alveolar discrete model and the solid surface of the pulmonary alveolar model, and a vertical negative pressure is applied to the outer surface of the solid region to drive the solid region to move, and the pressure is transmitted to the fluid region through the boundary layer, so that the fluid region is subjected to a force to generate fluid motion, so as to simulate the deformation of the pulmonary alveoli by the negative pressure and to cause the segmented internal flow field close to the pulmonary alveolar model in the airway flow field to move under the drive of the pulmonary alveolar deformation;

[0069] In the disclosed embodiment, the pulmonary alveoli are made of hyperelastic material. Therefore, applying negative pressure to the solid can simulate the force and deformation of the pulmonary alveoli in real situations.

[0070] In the disclosed embodiment, the pulmonary alveolar model adopts bidirectional coupling data transmission between flow field and solid, where solid transfers displacement to flow field simulation, and flow field transfers pressure to solid simulation. The fluid model grid adopts smoothing and grid re-division technology to prevent excessive grid deformation during simulation, which leads to non-convergence of calculation.

[0071] Among them, the breathing is simulated by stretching the alveoli with negative pressure, which is consistent with the normal breathing method. It avoids the abnormal situation such as bullae caused by directly giving the speed and starting positive pressure ventilation from G0. The flow field speed in the airway is less than 0.3 Mach, and incompressible fluid is used. The flow field temperature in the airway is constant, and its density is 1.225kg / m 3 , the viscosity is 1.7894×10 -5 kg(m·s).

[0072] Among them, a cosine-varying negative pressure is applied to the pulmonary alveolar model, and the key parameters such as velocity, pressure, and shear stress in the flow field are all time-dependent variables, so a non-steady-state simulation is adopted.

[0073] Specifically, after the meshing is completed, a bidirectional coupling data transfer mechanism is established between the discrete model of the pulmonary alveoli and the solid surface, and the deformation of the pulmonary alveoli during breathing and its influence on the flow field in the airway are simulated by applying cosine negative pressure.

[0074] Bidirectional coupling: The fluid pressure changes in the pulmonary alveolar discrete model can be fed back to the solid surface (i.e., the wall of the pulmonary alveoli) in real time, and the deformation of the solid surface will in turn affect the fluid flow in the flow field. This bidirectional coupling ensures the accuracy and authenticity of the simulation.

[0075] Cosine negative pressure application: In order to simulate the breathing process, a cosine waveform negative pressure is applied to the solid surface of the pulmonary alveolar model. This negative pressure changes with time, simulating the inhalation and exhalation process in the respiratory cycle. Under the action of negative pressure, the pulmonary alveolar model deforms, thereby driving the fluid flow inside it. At the same time, this deformation also affects the segments of the airway flow field close to the pulmonary alveolar model through a bidirectional coupling mechanism, causing the internal flow fields of these segments to produce corresponding movements driven by the deformation of the pulmonary alveoli.

[0076] In step S14, the fluid velocity at the designated level of each segment of the airway model is recorded in sequence from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and the fluid is transferred to the designated level of the previous segment of the segment in the airway model according to the velocity boundary condition, and the previous segment of the segment in the airway model is simulated for internal flow field motion, and the deformation of the last stage of the second level of the airway model is monitored to obtain pulmonary alveolar physical field data and the internal flow field motion of each segment is monitored to obtain internal flow field breathing data;

[0077] In the disclosed embodiment, the airway model is segmented from one end of the second-stage airway close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model. Each segment is carefully marked, and the fluid velocity is recorded at its designated position (i.e., "designated stage").

[0078] Record fluid velocity in segments: Use fluid dynamics software to set velocity monitoring points at specified levels of each segment. As the simulation progresses, these monitoring points will record the velocity of the fluid passing through that location in real time. For example, in the first segment near the lung acinus model, a monitoring point may be set at the intersection of the airway branches to record the fluid velocity at that point. Then, moving away from the lung acinus, monitoring points are also set at the corresponding positions in each subsequent segment.

[0079] Transfer of velocity boundary conditions and simulation of internal flow field motion: After recording the fluid velocities of a segment, these velocity data are used as boundary conditions and transferred to the specified level of the previous segment of this segment. In this way, the motion of the internal flow field of the previous segment can be simulated based on the new boundary conditions. For example, if the fluid velocity at the specified level of the first segment is v1, then this velocity v1 is used as the inlet velocity for simulating the corresponding position of the second segment. In this way, the motion of the internal flow field of the entire airway model is gradually simulated.

[0080] During the simulation process, not only the fluid motion within the airway model is concerned, but also the deformation of the pulmonary alveolus model is closely monitored. At the same time, the motion of the internal flow field of each segment is also monitored in real time.

[0081] Monitoring of pulmonary alveolus deformation: A special deformation monitoring algorithm is used to track the shape changes of the pulmonary alveolus model under negative pressure in real time. These change data (such as deformation amount, deformation rate, etc.) are recorded and used as important basis for evaluating the impact of the breathing process on lung tissue. For example, during the simulated inhalation phase, the pulmonary alveolus model may expand due to negative pressure, and the monitoring algorithm will accurately record this change process.

[0082] Monitoring of respiratory data of the internal flow field: Monitor the respiratory data of the internal flow field of each segment. These data include fluid velocity, pressure distribution, vortex formation, etc., which together reflect the dynamic changes of the internal flow field in the airway during the breathing process. For example, during the simulated exhalation phase, as the pressure in the airway decreases, the fluid velocity may slow down, and vortices may form in some areas of the airway. These changes will be monitored and recorded in real time.

[0083] In step S15, based on the fluid velocity at the specified level in each of the segments, the pulmonary alveolus physical field data, and the respiratory data of the internal flow field, a full-lung periodic breathing simulation result is generated.

[0084] Among them, the normal breathing frequency is 15 - 20 times / min. Therefore, a complete breathing cycle can be set as 4s, and the fluid velocity at the specified level in each of the segments, the pulmonary alveolus physical field data, and the respiratory data of the internal flow field are obtained to generate a full-lung periodic breathing simulation result.

[0085] In the embodiments of the present disclosure, after completing the recording of the fluid velocities of all segments, the simulation of the internal flow field motion, and the monitoring of the pulmonary alveolus deformation and the respiratory data of the internal flow field, these data are integrated to generate a simulation result of full-lung periodic breathing.

[0086] The data such as the fluid velocity, pressure distribution, vortex formation, etc. of each segment at different time points and the deformation data of the pulmonary alveolus model are sorted into time series data. These data together constitute the complete simulation result of full-lung periodic breathing.

[0087] By analyzing these data, we can gain a deeper understanding of the dynamic changes in the lungs during breathing. For example, we can observe the differences in fluid velocity between different segments, the deformation patterns of pulmonary alveoli during breathing, and how these changes affect fluid movement in the airways. This information is important for understanding the mechanisms of lung disease, evaluating treatment effects, and designing new treatments.

[0088] In order to show the simulation results more intuitively, visualization software is used to present the simulation process in the form of animation or charts. In this way, the complex changes in the lungs during breathing can be more intuitively understood.

[0089] In the disclosed embodiment, it is difficult to obtain an analytical solution for the motion equation of the fluid, and the discrete element method is used to solve the motion differential equation that cannot be solved directly. The complex geometric model is converted into a discrete structure composed of multiple grids for solution. For this purpose, the SIMPLE algorithm can be used, which assumes the initial pressure condition, uses the pressure field to drive the momentum equation to obtain the velocity field distribution, and then uses the velocity field to correct the pressure field. If the solution accuracy does not meet the actual convergence accuracy, repeat the above steps until the accuracy requirements are met.

[0090] The above technical solution constructs an asymmetric random whole lung model by adding random factors, which is closer to the actual situation. Then, by bidirectionally coupling data transmission between the flow field and solid in the pulmonary alveolar area, the flow field motion mode and deformation of the pulmonary alveoli in the real situation are simulated. Then, the flow field data is transmitted from bottom to top to the airway model G0 according to the velocity boundary conditions. In this way, negative pressure ventilation is adopted, which is the same as the real breathing situation, to avoid the situation that individual pulmonary alveoli are deformed and the airflow is inconsistent with the reality during positive pressure ventilation simulation. By adopting the bottom-up segmented whole lung simulation method, the anatomical parameters and boundary condition transmission positions of the corresponding lung lobes can be selected to simulate a single lung lobe. The whole lung fluid motion mode can be simulated under the real pulmonary alveoli under negative pressure breathing mode, thereby improving the accuracy and credibility of lung breathing simulation and simulation result verification. At the same time, it can achieve the reduction of calculation time in the whole lung simulation.

[0091] In a possible implementation, multiple first levels are constructed by scanning lung data, and multiple second levels are constructed by adding random factors to anatomical lung data to generate asymmetric structures, so as to form a multi-level airway model constructed by multiple first levels and multiple second levels, and a lung alveolar model is formed by clustering multiple spheres with preset diameters at the end of the second level of the airway model, including:

[0092] See also Figure 2 As shown, multiple first levels G0-G6 are constructed using the scanned lung data;

[0093] In the disclosed embodiment, in the initial stage of constructing a multi-level airway model, high-resolution CT (Computed Tomography) is used to scan three-dimensional image data of the lungs. These data contain detailed information on the internal structure of the lungs, such as the trachea, bronchi and their branches. Based on these scanned data, image processing technology and medical image segmentation algorithms are used to automatically or semi-automatically identify and extract the complete airway structure from the main bronchus (G0) to the sixth-generation bronchus (G6). Each generation of bronchi is divided based on its diameter and the complexity of its branches, where G0 represents the main bronchus, and as the number of generations increases, the bronchi gradually become thinner and go deeper into the lungs.

[0094] For example, suppose that in a CT scan image, the main trachea (G0) is clearly visible, with a diameter of about 18 mm, which then bifurcates into two main bronchi (G1) on the left and right, each of which further bifurcates into thinner bronchus (G2), and so on, until the sixth generation bronchus (G6). The diameter of these bronchi gradually decreases from 18 mm in G0 to a few millimeters in G6, forming the first part of the multi-level airway model, namely the G0-G6 level.

[0095] See also Figure 3 , Figure 4 and Figure 5 As shown, the anatomical lung data is added with random factors to generate an asymmetric structure to construct multiple second levels of G6-G23, forming a multi-level airway model composed of 7 first levels and 17 second levels;

[0096] In the disclosed embodiment, after the construction of multiple first levels (G0-G6) is completed, in order to more realistically simulate the complexity and asymmetry of the lung airway, known anatomical knowledge and statistical data are used, combined with random factors (such as random changes in branch angles, random increases and decreases in the number of branches, etc.), to generate bronchioles and alveolar ducts from G6 to G23 (collectively referred to as the second level). The introduction of random factors is intended to simulate the natural variation of lung structure between individuals and abnormal changes in disease states.

[0097] For example, based on G6, each bronchus may branch out into multiple thinner bronchi (G7) at different angles, and the branching angles and diameters of these G7 bronchi after bifurcation may vary due to the influence of random factors. For example, in some areas, due to the effect of random factors, G7 may branch out into two thinner bronchi, while in another area, at the same level, they branch out into bronchi of the same diameter. As the number of generations increases, the differences between individuals will become smaller and closer to the theoretical value until G23, when the airway is close to the alveolar area, the diameter is further reduced, and the morphology is more complex.

[0098] At the end of the airway model G23, the pulmonary alveolar model is formed by clustering a plurality of spheres of the preset diameter;

[0099] In the disclosed embodiment, at the end of the airway model, i.e., after the G23 level, the entire model is improved by simulating the structure of the pulmonary alveoli (the basic functional unit composed of multiple alveoli). Since the diameter and shape of the alveoli are relatively consistent, they can be approximately represented by a series of spherical structures with preset diameters. These spherical structures are arranged in a cluster at the end of the G23 level airway to form a pulmonary alveolar model.

[0100] For example, at each end of the G23-level airway, a series of spherical structures with a diameter of about 0.2-0.5 mm are preset according to the anatomical characteristics of the pulmonary alveoli. These spherical structures are closely arranged to simulate the tight connection between the alveoli. Finally, these spherical clusters are connected to the G23-level airway to form a complete multi-level lung airway and pulmonary alveolar model.

[0101] Among them, multiple second levels of G6-G23 are divided into segments G6-G13, G13-G20, and G20-G23.

[0102] That is to say, the airway model consists of 24 levels, among which G0-G6 are obtained by Micro CT scanning, and the corresponding real CAD lung model is obtained after surface smoothing; G6-G23 is an asymmetric model generated by building a mathematical model based on anatomical data, which can be specifically divided into three sections: G6-G13, G13-G20, and G20-G23. The end of G23 is spliced ​​with a pulmonary alveolar model, which is composed of 25 spherical clusters with a diameter of 0.25 mm.

[0103] It can be explained that the airway diameter of the G6-G13 segment is relatively large, with fewer branches, and is mainly responsible for gas transportation; while the G13-G20 segment gradually transitions to smaller airways with more branches, and gas exchange begins to become important; in the G20-G23 segment, the airway is very close to the alveoli, and the gas exchange efficiency reaches the highest. Through this segmentation, the airway function and pathological changes in different regions can be studied more specifically.

[0104] In a possible implementation, in step S14, the fluid velocity at a specified level in each of the segments of the airway model is sequentially recorded in segments from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and the fluid is transferred to the specified level of the previous segment of the segment in the airway model according to the velocity boundary condition, and the previous segment of the segment in the airway model is simulated for internal flow field motion, and the deformation of the second stage of the airway model is monitored at the end to obtain pulmonary alveolar physical field data and monitor the internal flow field motion of each segment to obtain internal flow field breathing data, including:

[0105] In step S141, the velocity of the fluid at G20 of the G20-G23 segment in the airway model is recorded, and the fluid is transferred to G20 of the G13-G20 segment in the airway model according to the velocity boundary condition, and the internal flow field motion simulation of the G13-G20 segment is performed, and the deformation of the second-stage end of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G13-G20 segment is monitored to obtain the internal flow field breathing data of the G13-G20 segment;

[0106] In the embodiment of the present disclosure, in the constructed multi-level airway model, especially in the G20-G23 segment which is fine and close to the alveoli, the transmission and distribution of gas in the lungs can be understood based on the change in fluid velocity. Therefore, it is first necessary to set a monitoring point at G20 of the G20-G23 segment, and use computational fluid dynamics (CFD) software or experimental equipment (such as particle image velocimetry PIV) to record the fluid velocity at this point.

[0107] For example, suppose that at G20 in the G20-G23 segment, the fluid velocity measured by FSI simulation or experiment is 0.01 m / s. This velocity reflects the initial velocity state of the gas at this location when it enters this subdivision from a higher-level gas channel.

[0108] After obtaining the fluid velocity at G20 in the G20-G23 segment, this velocity is used as a boundary condition and applied to the G20 point at the junction of the G13-G20 segment and the G20-G23 segment. This can simulate the continuous flow process of the gas in the airway and ensure that the impact on the upstream fluid can be taken into account when simulating the flow field movement in the G13-G20 segment. For example, the previously recorded fluid velocity of 0.5 m / s is used as a boundary condition and input into the CFD simulation software, and this velocity is set as the inlet velocity of the G20 point at the junction of the G13-G20 segment and the G20-G23 segment. In this way, when simulating the internal flow field movement of the G13-G20 segment, the flow of the gas in the segment will be calculated based on this velocity boundary condition.

[0109] The internal flow field motion simulation of the G13-G20 segment is performed using CFD software. Considering the geometric shape of the airway, the physical properties of the fluid (such as density, viscosity) and boundary conditions (such as inlet velocity, outlet pressure, etc.), the internal flow field characteristics such as the flow velocity, pressure distribution, and vortex of the gas in the segment are calculated. For example, in the CFD software, after setting the geometric model of the airway, the physical parameters of the fluid and the boundary conditions, the simulation calculation is started. According to these input conditions, the details of the gas flow in the G13-G20 segment are calculated by solving fluid dynamics equations such as the Navier-Stokes equations. The simulation results may include velocity vector diagrams, pressure cloud diagrams, vortex distribution diagrams, etc. of the fluid, which will help to understand the flow characteristics of the gas in the segment.

[0110] During the simulation of gas flow, the stage G23 at the end of the second stage of the airway model may be deformed due to the influence of gas pressure. In order to simulate this process more accurately, it is necessary to monitor the deformation of the stage G23 at the end of the second stage of the airway model during the simulation and record the relevant deformation data. For example, in the FSI simulation, a series of monitoring points or monitoring surfaces can be set around the pulmonary alveolar model to capture the pressure changes caused by gas flow on the pulmonary alveoli. As the simulation proceeds, the pressure distribution on these monitoring points or monitoring surfaces is calculated, and the deformation of the pulmonary alveoli is inferred based on this. The dynamic response of the pulmonary alveoli during gas flow can be evaluated by observing the deformation animation in the simulation results or extracting deformation data (such as deformation amount, deformation rate, etc.).

[0111] After completing the internal flow field motion simulation of the G13-G20 segment, the internal flow field respiratory data of the segment during the simulation process is extracted. These data include but are not limited to the flow velocity, pressure distribution, vortex characteristics, etc. of the gas, which together describe the flow state of the gas in the segment and the dynamic characteristics of the breathing process. For example, through the post-processing function of the CFD software, a series of internal flow field data of the G13-G20 segment during the simulation process can be extracted. These data may be presented in the form of charts, cloud maps or animations, showing the flow velocity distribution of the gas in the segment, the change of pressure gradient, and the formation and dissipation of vortices. By analyzing these data, we can gain an in-depth understanding of the gas transmission mechanism in the pulmonary airway, respiratory efficiency, and possible pathological changes.

[0112] In step S142, the velocity of the fluid at G13 of the G13-G20 segment in the airway model is recorded, and the fluid is transferred to G13 of the G6-G13 segment in the airway model according to the velocity boundary condition, and the internal flow field motion simulation of the G6-G13 segment is performed, and the deformation of the second-stage end of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G6-G13 segment is monitored to obtain the internal flow field respiratory data of the G6-G13 segment;

[0113] In the disclosed embodiment, the fluid velocity of the G13-G20 segment at G13 is recorded, and then this velocity is used as a boundary condition to simulate the airflow movement of the G6-G13 segment, and the deformation of the pulmonary alveoli in this process is monitored simultaneously.

[0114] Record the fluid velocity: Assume that the fluid velocity measured at G13 of the G13-G20 segment is 0.15 m / s through CFD simulation or experimental measurement. This velocity reflects the initial state of gas flowing from the higher-level gas channel into the G6-G13 segment.

[0115] Set boundary conditions: Set this speed value as the speed boundary condition of the G6-G13 segment at the G13 entrance and input it into the CFD simulation software.

[0116] Internal flow field motion simulation: Based on the input geometric model, fluid parameters and boundary conditions, start simulating the airflow motion in the G6-G13 segment. During the simulation, calculate the airflow velocity, pressure, vortex and other parameters, and generate corresponding visualization results.

[0117] Deformation monitoring of the last stage G23 of the second stage of the airway model: the changes start from G20-G23, and then affect upwards in sequence. Therefore, corresponding monitoring points or monitoring areas are set in the simulation to evaluate the possible deformation effects of airflow on the last stage G23 of the second stage of the airway model. This effect can be indirectly reflected by simulating the pressure changes and airflow distribution in the pulmonary alveolar region. Extract the internal flow field respiratory data of the G6-G13 segment, including airflow velocity distribution, pressure cloud map, vortex characteristics, etc., and simultaneously obtain indirect data on the deformation of the downstream pulmonary alveoli (such as the pressure change).

[0118] In step S143, the fluid velocity at G6 of the G6-G13 segment in the airway model is recorded, and the fluid is transferred to G6 of the G0-G6 segment in the airway model according to the velocity boundary condition, and the internal flow field motion of the G0-G6 segment is simulated, and the deformation of the second-stage end of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G0-G6 segment is monitored to obtain the internal flow field respiratory data of the G0-G6 segment.

[0119] In the disclosed embodiment, the fluid velocity of the G6-G13 segment at G6 is recorded, and then this velocity is used as a boundary condition to simulate the airflow movement of the G0-G6 segment, and the deformation of the pulmonary alveoli that may be involved in this process is also monitored.

[0120] Record fluid velocity: Based on the simulation results of step S142, the fluid velocity measured at G6 of the G6-G13 segment is 0.7 m / s (this value may vary depending on factors such as airflow resistance and branching effect in the simulation).

[0121] Set boundary conditions: Set this velocity value as the velocity boundary condition of the G0-G6 segment at the G6 entrance and input it into the CFD simulation software.

[0122] Internal flow field motion simulation: Similar to step S142, simulate the airflow motion in the G0-G6 segment, calculate relevant parameters and generate visualization results.

[0123] Deformation monitoring of the G23 at the end of the second stage of the airway model: Since the G0-G6 segment is closer to the entrance of the airway, fewer pulmonary alveoli are directly involved, but the dynamic changes of the airflow in this area affect the deformation of the G23 at the end of the second stage of the airway model. By setting monitoring points, factors that may affect the downstream pulmonary alveoli, such as the pressure distribution of the airflow on the airway wall and the stability of the airflow, are evaluated.

[0124] Among them, since the axial velocity v of the fluid velocity in the tube satisfies the following formula, there must be a repetition between each segment (for example, G6-G13 and G13-G20, G13 is repeated). Only fully developed fluids will satisfy this formula, and the specified velocity boundary is a constant, which will be consistent with the actual situation only after development.

[0125]

[0126] Among them, R is the radius of the circular tube where the fluid is located, r is the distance between the fluid and the axis of the circular tube, μ is the kinematic viscosity coefficient, dp is the pressure drop along the tube, and dl is the displacement along the tube.

[0127] The above technical solution simulates the internal flow field motion and monitors the deformation of the pulmonary alveoli by gradually recording and transmitting the fluid velocities of different segments (G20-G23 to G0-G6) in the airway model as boundary conditions. It can accurately simulate the dynamic changes of airflow in the airway and its impact on the pulmonary alveoli, and obtain detailed pulmonary alveolar physical field data and internal flow field respiratory data. This can get close to the real breathing mechanism, deeply understand the breathing mechanism, evaluate the health of the lungs, and provide an important basis for the research and diagnosis of respiratory diseases.

[0128] In one possible implementation, the preset diameter is 0.25 mm, and the preset number is 25.

[0129] In a possible implementation, in step S15, generating a whole lung periodic breathing simulation result according to the fluid velocity at a specified level in each of the segments, the pulmonary alveolar physical field data and the internal flow field breathing data includes:

[0130] According to whether the tidal volume and residual volume in the internal flow field respiratory data are respectively within the preset value range, the lung function index verification simulation result is obtained, wherein the preset value range corresponding to the tidal volume is 400-600ml, and the preset value range corresponding to the residual volume is 2000-2400ml;

[0131] In the embodiment of the present disclosure, according to the flow field parameters in the airway model, the tidal volume is 504 ml, the residual volume is 2.3 L, and the lung compliance is 0.202 L / cm H 2 O, within the range of normal lung function data. It can be seen that the airway shear force and flow field velocity decrease significantly as the airway penetrates into the small bronchi, which is the same as the real lung, and the correctness of the model can be verified.

[0132] In step S153, a deformation verification simulation result is obtained according to the pulmonary alveolar physical field data;

[0133] In the disclosed embodiment, the accuracy and reliability of the model are verified based on the pulmonary alveolar volume change, stress-strain distribution, and flow field motion pattern in the pulmonary alveolar physical field data to ensure that the simulation results can truly reflect the dynamic changes of the lungs during the breathing process.

[0134] For example, the simulated model gas flow is compared with the respiratory gas flow in the known prior physiological parameters of the real human body. If the difference between the two is within an acceptable range, it can be considered that the model is accurate in simulating volume changes.

[0135] Analyze the simulated pulmonary alveolar stress-strain distribution diagram to evaluate whether it is reasonable. Pay attention to whether the high stress area is located in the weak link of the lung tissue and whether the strain distribution is uniform. By comparing with known biomechanical data or experimental results, the accuracy of the model in terms of mechanical behavior can be verified.

[0136] Observe the simulated pulmonary alveolar flow field motion pattern to determine whether it conforms to the physiological laws of gas transmission and exchange in the lungs. Pay attention to whether the airflow is smooth, whether the vortex is reasonably distributed, and whether the gas exchange efficiency is high. Through comparative analysis, the simulation effect of the model in terms of flow field motion pattern can be evaluated.

[0137] In step S154, a whole-lung periodic breathing simulation result is generated according to the speed verification simulation result, the lung function index verification simulation result and the deformation verification simulation result.

[0138] In the disclosed embodiment, the obtained deformation verification simulation results, velocity verification simulation results and lung function index verification simulation results are combined to generate the simulation results of whole lung periodic breathing. The simulation results of different parts are integrated and optimized to ensure the consistency and accuracy of the whole simulation process.

[0139] For example, the airflow velocity distribution in the velocity verification simulation results is combined with the gas volume change in the lung function index verification simulation results. The post-processing function in the software is used to overlay the two in the same view, so as to more intuitively observe the dynamic relationship between airflow and gas volume.

[0140] Next, the simulation results of the pulmonary alveolar deformation verification were integrated into the whole lung model. This included mapping the simulated pulmonary alveolar volume changes, stress and strain distribution, and flow field motion patterns to the whole lung model to reflect the overall deformation of the lungs during breathing.

[0141] Finally, run the whole lung periodic breathing simulation program and perform simulation calculations based on the integrated data and model parameters. The simulation process will consider the impact of changes in physiological factors such as breathing frequency, breathing depth, and airway resistance on the lung breathing process. The simulation results will show the dynamic changes of the whole lung during periodic breathing, including airflow distribution, gas exchange, and deformation of lung tissue.

[0142] In a possible implementation, the Reynolds number of the internal flow field in the first stage of the airway model is greater than a preset turbulence threshold, and the Reynolds number of the internal flow field in the second stage of the airway model is less than the preset turbulence threshold;

[0143] In this example, the airway model can be regarded as flow in the tube. Considering the turbulence and laminar flow phenomena in the actual flow field, the turbulence and laminar flow phenomena are distinguished according to the Reynolds number (Re):

[0144]

[0145] Wherein u is the relative velocity between the airflow inside the airway model and the airway model, d is the diameter of the pipe where the airflow is located, and ν is the kinematic viscosity coefficient of the gas.

[0146] The first stage of the airway model adopts a low Reynolds number corrected SST k-ω viscosity model, and the second stage of the airway model adopts a laminar viscosity model;

[0147] When the dimensionless number Re < 2300, the influence of fluid viscous force on the flow field is greater than the inertial force, and the disturbances in the flow field will decay due to viscous force, thus forming a stable laminar flow phenomenon. When 2300 < Re < 8000, the viscous force and the inertial force act equivalently, which is the transition stage from laminar flow to turbulent flow. When Re > 8000, the fluid is mainly dominated by inertial force and is in an unsteady state. After the appearance of disturbances, they will be amplified and then form turbulent flow.

[0148] Turbulent flow mostly occurs in areas with relatively large air flow velocities, which will lead to the exchange of momentum and mass in the flow field and form vortices, which will have a greater impact on the solution of the motion equation of the flow field in the airway. After the flow field in the airway is disturbed by the narrow area of the nasal cavity, it enters the G0-G6 stage with a fast air flow velocity and a large airway diameter, where turbulent flow phenomenon occurs. As the air flow deepens, the airway branches gradually increase, and the gas flow velocity and airway diameter decrease. Therefore, the laminar viscous model is adopted in the G6-G23 stage.

[0149] Among them, the control equations of the low Reynolds number corrected SST k-ω viscous model are as follows:

[0150]

[0151] Among them, β = β 0 f β ;

[0152] α k = α ω = 0.5;

[0153] Among them, S ij is the mean velocity strain rate tensor, δ ij is the Kronecker operator, ρ is the fluid density, ν T is the turbulent viscosity, u i , u j (i, j = 1, 2, 3) respectively represent the velocities in the x, y, and z directions, k is the turbulent kinetic energy, and ω is the specific dissipation rate.

[0154] In a possible implementation manner, the discriminant formula of the cosine negative pressure P is:

[0155]

[0156] Among them, t is the time, and π is a mathematical constant.

[0157] In a possible implementation manner, the pulmonary alveolus physical field data includes at least one of the following: the change amount of pulmonary alveolus volume, the stress and strain distribution of pulmonary alveolus, and the motion mode of the pulmonary alveolus discrete model.

[0158] Among them, the pulmonary alveoli are the basic functional units of the lungs, consisting of multiple alveoli and the interstitial tissues around them. During the breathing process, as the gas is inhaled and exhaled, the alveoli will expand and contract, causing the volume of the pulmonary alveoli to change. The change in pulmonary alveolar volume refers to the increase or decrease in the volume of the pulmonary alveoli caused by gas exchange within a certain period of time. The change in pulmonary alveolar volume can be used to evaluate the respiratory function of the lungs, gas exchange efficiency, and changes in the compliance of lung tissue under disease conditions.

[0159] Pulmonary alveolar stress-strain is used to describe the deformation of pulmonary alveoli when subjected to external forces. In the lungs, when gas enters the alveoli and expands them, the alveolar wall will be subjected to a certain tensile force, namely stress; at the same time, the alveolar wall will deform, namely strain. Pulmonary alveolar stress-strain distribution refers to the distribution of stress and strain generated by gas pressure in the entire pulmonary alveolar area. Pulmonary alveolar stress-strain distribution can be used to understand the mechanical behavior of lung tissue during breathing, including its strength, toughness, and possible damage or remodeling processes.

[0160] The motion pattern of the pulmonary alveolar discrete model refers to the flow mode and characteristics of the fluid in the pulmonary alveoli. For the pulmonary alveolar model, the flow field motion pattern describes the dynamic processes of the gas flow path, velocity distribution, vortex formation and dissipation in the alveoli and the surrounding airways. It can be used to understand the efficient transmission and effective exchange mechanism of gas in the lungs. By simulating the flow field motion pattern of the pulmonary alveolar model, the efficiency and effect of gas flow under different respiratory conditions and physiological conditions (such as normal breathing, deep breathing, disease state, etc.) can be evaluated, thereby providing a scientific basis for the diagnosis and treatment of lung diseases.

[0161] The present disclosure also provides a whole lung periodic breathing simulation device, see Figure 6 As shown, it includes: a construction module 610 is configured to construct a plurality of first levels with scanned lung data, and to construct a plurality of second levels with asymmetric structures generated by adding random factors to anatomical lung data, so as to form a multi-level airway model constructed by a plurality of first levels and a plurality of second levels, and to form a lung acinar model at the end of the second level of the airway model by clustering a plurality of spheres with a preset diameter, wherein the plurality of the second levels are divided into a plurality of segments;

[0162] The flow field generation module 620 is configured to perform grid division on the internal flow field of the airway model to generate the internal flow field of the airway and to perform grid division on the pulmonary alveolar model to divide the fluid and solid in the pulmonary alveolar model to generate a pulmonary alveolar discrete model;

[0163] The pressure module 630 is configured to establish a bidirectional coupling data transmission between the pulmonary alveolar discrete model and the solid surface of the pulmonary alveolar model, and to drive the solid area to move by applying a vertical negative pressure to the outer surface of the solid area, and to transfer it to the fluid area through the boundary layer, so that the fluid area is subjected to force and fluid motion occurs, so as to simulate the deformation of the pulmonary alveoli by the negative pressure and to make the segmented internal flow field close to the pulmonary alveolar model in the airway flow field move under the drive of the pulmonary alveolar deformation;

[0164] The simulation and recording module 640 is configured to sequentially record the fluid velocity at a specified level in each of the segments of the airway model from the second level close to the lung alveolar model to the direction away from the lung alveolar model, and according to the velocity boundary condition, transfer the fluid to the specified level of the previous segment of the segment in the airway model, simulate the internal flow field motion of the previous segment of the segment in the airway model, and monitor the deformation of the second-stage end of the airway model to obtain the lung alveolar physical field data and monitor the internal flow field motion of each segment to obtain the internal flow field breathing data;

[0165] The result generation module 650 is configured to generate a whole-lung periodic breathing simulation result according to the fluid velocity at a specified level in each of the segments, the lung acinar physical field data and the internal flow field breathing data.

[0166] In a possible implementation, the construction module 610 is configured to construct multiple first levels G0-G6 using the scanned lung data; construct multiple second levels G6-G23 using the anatomical lung data by adding random factors to generate an asymmetric structure, forming a multi-level airway model consisting of 7 first levels and 17 second levels; and form the lung alveolar model at the end of the airway model G23 by clustering multiple spheres of the preset diameter;

[0167] Among them, multiple second levels of G6-G23 are divided into segments G6-G13, G13-G20, and G20-G23.

[0168] In one possible implementation, the simulation and recording module 640 is configured as follows:

[0169] The velocity of the fluid at G20 of the G20-G23 segment in the airway model is recorded, and the fluid is transferred to G20 of the G13-G20 segment in the airway model according to the velocity boundary condition, and the internal flow field motion simulation of the G13-G20 segment is performed, and the deformation of the second stage of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G13-G20 segment is monitored to obtain the internal flow field breathing data of the G13-G20 segment;

[0170] The velocity of the fluid at G13 of the G13-G20 segment in the airway model is recorded, and the fluid is transferred to G13 of the G6-G13 segment in the airway model according to the velocity boundary condition, and the internal flow field motion simulation of the G6-G13 segment is performed, and the deformation of the second stage of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G6-G13 segment is monitored to obtain the internal flow field respiratory data of the G6-G13 segment;

[0171] The fluid velocity at G6 of the G6-G13 segment in the airway model is recorded, and the fluid is transferred to G6 of the G0-G6 segment in the airway model according to the velocity boundary condition, and the internal flow field motion of the G0-G6 segment is simulated, and the deformation of the second-most stage of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G0-G6 segment is monitored to obtain the internal flow field respiratory data of the G0-G6 segment.

[0172] In a possible implementation, the preset diameter is 0.25 mm and the preset number is 25.

[0173] In a possible implementation, the result generation module 650 is configured to:

[0174] According to whether the tidal volume and residual volume in the internal flow field respiratory data are respectively within the preset value range, the lung function index verification simulation result is obtained, wherein the preset value range corresponding to the tidal volume is 400-600ml, and the preset value range corresponding to the residual volume is 2000-2400ml;

[0175] The simulation results are verified according to the lung function index, and the whole lung periodic breathing simulation results are generated.

[0176] In a possible implementation, the Reynolds number of the internal flow field in the first stage of the airway model is greater than a preset turbulence threshold, and the Reynolds number of the internal flow field in the second stage of the airway model is less than the preset turbulence threshold;

[0177] The first stage of the airway model adopts a low Reynolds number corrected SST k-ω viscosity model, and the second stage of the airway model adopts a laminar viscosity model;

[0178] The control equation of the low Reynolds number corrected SST k-ω viscosity model is as follows:

[0179]

[0180] in, β=β 0 f β ;

[0181] α k =α ω =0.5;

[0182] Among them, S ij is the average velocity strain rate tensor, δ ij is the Kronecker operator, ρ is the fluid density, ν T is the turbulent viscosity, u i ,u j (i, j = 1, 2, 3) represent the velocities in the x, y, and z directions respectively, k is the turbulent kinetic energy, and ω is the specific dissipation rate.

[0183] In a possible implementation, the discriminant formula of the cosine negative pressure P is:

[0184]

[0185] Among them, t is time and π is a mathematical constant.

[0186] In a possible implementation, the pulmonary alveolar physical field data includes at least one of the following: pulmonary alveolar volume change, pulmonary alveolar stress and strain distribution, and pulmonary alveolar discrete model motion mode.

[0187] An embodiment of the present disclosure also provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions stored in the memory to implement any of the methods described in the aforementioned embodiments.

[0188] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0189] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A whole-lung periodic breathing simulation method, characterized in that: include: Constructing multiple first levels with scanned lung data, adding random factors to anatomical lung data to generate asymmetric structures to construct multiple second levels, forming a multi-level airway model constructed by multiple first levels and multiple second levels, and forming a lung alveolar model at the end of the second level of the airway model through multiple spherical clusters with preset diameters, wherein the multiple second levels are divided into multiple segments; Meshing the internal flow field of the airway model to generate the internal flow field of the airway and meshing the pulmonary alveolar model to divide the fluid and solid in the pulmonary alveolar model to generate a pulmonary alveolar discrete model; Establishing a bidirectional coupling data transmission between a pulmonary alveolar discrete model and a solid surface of the pulmonary alveolar model, and applying a vertical negative pressure to the outer surface of the solid region to drive the solid region to move, and transmitting the pressure to the fluid region through the boundary layer, so that the fluid region is subjected to a force to generate fluid motion, so as to simulate the deformation of the pulmonary alveoli by the negative pressure and to make the segmented internal flow field close to the pulmonary alveolar model in the airway flow field generate motion driven by the deformation of the pulmonary alveoli; The fluid velocity at the designated level of each segment of the airway model is sequentially recorded in segments from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and the fluid is transferred to the designated level of the previous segment of the segment in the airway model according to the velocity boundary condition, and the previous segment of the segment in the airway model is simulated for internal flow field motion, and the deformation of the last stage of the second level of the airway model is monitored to obtain pulmonary alveolar physical field data and the internal flow field motion of each segment is monitored to obtain internal flow field breathing data; A whole-lung periodic breathing simulation result is generated according to the fluid velocity at a specified level in each of the segments, the lung acinus physical field data and the internal flow field breathing data.

2. The method according to claim 1, characterized in that The method comprises: constructing a plurality of first levels by scanning lung data, adding random factors to anatomical lung data to generate asymmetric structures to construct a plurality of second levels, forming a multi-level airway model constructed by a plurality of first levels and a plurality of second levels, and forming a lung alveolar model by clustering a plurality of spheres with preset diameters at the end of the second level of the airway model, including: Constructing multiple first levels G0-G6 with the scanned lung data; Adding random factors to the anatomical lung data to generate an asymmetric structure to construct multiple second levels of G6-G23, forming a multi-level airway model constructed of 7 first levels and 17 second levels; At the end of the airway model G23, the pulmonary alveolar model is formed by clustering a plurality of spheres of the preset diameter; Among them, multiple second levels of G6-G23 are divided into segments G6-G13, G13-G20, and G20-G23.

3. The method according to claim 2, characterized in that The fluid velocity at the specified level of each segment of the airway model is sequentially recorded in segments from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and the fluid is transferred to the specified level of the previous segment of the segment in the airway model according to the velocity boundary condition, and the previous segment of the segment in the airway model is simulated for the internal flow field motion, and the deformation of the second stage of the airway model is monitored at the end to obtain the pulmonary alveolar physical field data and the internal flow field motion of each segment is monitored to obtain the internal flow field breathing data, including: The velocity of the fluid at G20 of the G20-G23 segment in the airway model is recorded, and the fluid is transferred to G20 of the G13-G20 segment in the airway model according to the velocity boundary condition, and the internal flow field motion of the G13-G20 segment is simulated, and the deformation of G23 at the end of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G13-G20 segment is monitored to obtain the internal flow field respiratory data of the G13-G20 segment; The velocity of the fluid at G13 of the G13-G20 segment in the airway model is recorded, and the fluid is transferred to G13 of the G6-G13 segment in the airway model according to the velocity boundary condition, and the internal flow field motion of the G6-G13 segment is simulated, and the deformation of G23 at the end of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G6-G13 segment is monitored to obtain the internal flow field respiratory data of the G6-G13 segment; The fluid velocity at G6 of the G6-G13 segment in the airway model is recorded, and the fluid is transferred to G6 of the G0-G6 segment in the airway model according to the velocity boundary condition, the internal flow field motion of the G0-G6 segment is simulated, and the deformation of G23 at the end of the airway model is monitored to obtain the pulmonary alveolar physical field data and the internal flow field motion of the G0-G6 segment is monitored to obtain the internal flow field respiratory data of the G0-G6 segment.

4. The method according to claim 2, characterized in that: The preset diameter is 0.25 mm, and the preset number is 25.

5. The method according to claim 1, characterized in that The step of generating a whole lung periodic breathing simulation result according to the fluid velocity at a specified level in each of the segments, the pulmonary alveolar physical field data, and the internal flow field breathing data comprises: According to whether the tidal volume and residual volume in the internal flow field respiratory data are respectively within the preset value range, the lung function index verification simulation result is obtained, wherein the preset value range corresponding to the tidal volume is 400-600ml, and the preset value range corresponding to the residual volume is 2000-2400ml; The simulation results are verified according to the lung function index, and the whole lung periodic breathing simulation results are generated.

6. The method according to claim 1, characterized in that The Reynolds number of the internal flow field in the first stage of the airway model is greater than a preset turbulence threshold, and the Reynolds number of the internal flow field in the second stage of the airway model is less than the preset turbulence threshold; The first stage of the airway model adopts a low Reynolds number corrected SST k-ω viscosity model, and the second stage of the airway model adopts a laminar viscosity model; The control equation of the low Reynolds number corrected SST k-ω viscosity model is as follows: Among them, a k =a ω =0.5; Among them, S ij is the average velocity strain rate tensor, δ ij is the Kronecker operator, ρ is the fluid density, ν T is the turbulent viscosity, u i ,u j (i, j = 1, 2, 3) represent the velocities in the x, y, and z directions respectively, k is the turbulent kinetic energy, and ω is the specific dissipation rate.

7. The method according to claim 1, characterized in that The discriminative formula of the cosine negative pressure P is: Among them, t is time and π is a mathematical constant.

8. The method according to any one of claims 1 to 7, characterized in that The pulmonary alveolar physical field data includes at least one of the following: pulmonary alveolar volume change, pulmonary alveolar stress and strain distribution, and pulmonary alveolar discrete model motion mode.

9. A whole-lung periodic breathing simulation device, characterized in that: include: A construction module is configured to construct a plurality of first levels using scanned lung data, and to construct a plurality of second levels using anatomical lung data to generate an asymmetric structure by adding random factors, so as to form a multi-level airway model constructed by the plurality of first levels and the plurality of second levels, and to form a lung alveolar model at the end of the second level of the airway model by clustering a plurality of spheres of preset diameters, wherein the plurality of the second levels are divided into a plurality of segments; A flow field generation module is configured to mesh the internal flow field of the airway model to generate the airway internal flow field and mesh the pulmonary alveolar model to respectively divide the fluid and solid in the pulmonary alveolar model to generate a pulmonary alveolar discrete model; A pressure module is configured to establish a bidirectional coupling data transmission between a pulmonary alveolar discrete model and a solid surface of the pulmonary alveolar model, and to drive the solid area to move by applying a vertical negative pressure to the outer surface of the solid area, and to transfer the pressure to the fluid area through the boundary layer, so that the fluid area is subjected to a force to generate fluid motion, so as to simulate the deformation of the pulmonary alveoli by the negative pressure and to make the segmented internal flow field close to the pulmonary alveolar model in the airway flow field generate motion under the drive of the pulmonary alveolar deformation; The simulation and recording module is configured to sequentially record the fluid velocity at a specified level in each of the segments of the airway model from the second level close to the pulmonary alveolar model to the direction away from the pulmonary alveolar model, and according to the velocity boundary condition, transfer the fluid to the specified level of the previous segment of the segment in the airway model, simulate the internal flow field motion of the previous segment of the segment in the airway model, and monitor the deformation of the second-stage end of the airway model to obtain pulmonary alveolar physical field data and monitor the internal flow field motion of each segment to obtain internal flow field breathing data; The result generating module is configured to generate a whole lung periodic breathing simulation result according to the fluid velocity at a specified level in each of the segments, the lung acinus physical field data and the internal flow field breathing data.

10. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

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