A numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and its construction method

Abstract

This application relates to the field of intelligent medicine, and specifically relates to a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and a method for constructing the same. The method includes obtaining image data of the respiratory tract of an animal, particle properties, fluid flow pattern types, and respiratory tidal volume; performing three-dimensional reconstruction on the respiratory tract region through the image data to obtain a respiratory tract model; constructing a particle motion tracking model by analyzing the forces acting on the particles based on the fluid flow pattern types and particle properties; constructing a respiratory tract deformation model by calculating the deformation amount of the respiratory tract of the respiratory tract model through the respiratory tidal volume; and combining the respiratory tract model, the particle motion tracking model, and the respiratory tract deformation model to obtain a numerical model for the deposition distribution of heterogeneous particles in the respiratory tract of animals. This application can simulate the deposition distribution and deposition amount of heterogeneous particles in different regions of the respiratory tract, and has good application value.

Description

Technical Field

[0001] The present application relates to the field of intelligent medicine, and particularly relates to a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals, and a construction method, device, system and computer-readable storage medium thereof. Background Art

[0002] In recent years, respiratory diseases have become the second largest diseases endangering human health. Targeted drug delivery is widely used in the treatment of respiratory diseases. The efficacy of targeted drug delivery depends on the total deposition dose of the drug in the lung and the local deposition dose of the target lung tissue. Animal experiments in vivo are a prerequisite before the clinical drug is launched. Therefore, it is of great significance to understand the deposition law and mechanism of particulate matter in the respiratory tract of animals. However, due to the extremely complex structure of the respiratory tract, which is a non-transparent tissue, and affected by the existing technology, current animal experiment studies can only obtain the total deposition rate and regional deposition rate of particulate matter in animals, and cannot obtain the high-resolution deposition distribution of particulate matter in different regions. Ye et al. studied the deposition distribution of 2μm polydisperse particulate matter aerosol in the respiratory tract of guinea pigs. After observation by paraffin-embedded sections, it was found that most of the 2μm particulate matter was deposited in the lower respiratory tract region. Philip et al. studied the inhalation of 0.5μm, 1μm, 3μm, 5μm radioactive polydisperse aerosol ( 99m Tc) by rats and mice through oral and nasal exposure, and quantitatively analyzed the regional deposition rate by using single photon emission computed tomography / X-ray computed tomography technology (SPECT / CT). It was found that as the particle size increased, the total deposition rate in the respiratory tract decreased, the deposition rate in the upper respiratory tract increased, and the deposition rate in the lung decreased. However, the aerosol particles in the above experiments were all polydisperse distributions (GSD>1.5), and the particle size span was relatively large. For example, the actual range of the 5μm aerosol particle size was 1μm to 15μm. Therefore, it was impossible to accurately reflect the movement and deposition law of a single particle size, and at the same time, it was impossible to construct a numerical model of particulate matter deposition, and thus it was impossible to obtain the deposition mechanism of heterogeneous particle size particulate matter. In addition, due to the influence of the previous technology, it was difficult to reconstruct the geometric model of the animal respiratory tract. Therefore, there were few reports on the numerical study of particle deposition distribution in the respiratory tract of small animals. Moreover, the respiratory tract is a high-humidity environment. Hydrophilic particulate matter will grow rapidly when it enters the respiratory tract. It is very easy to have particle collision and aggregation with the flow of the respiratory airflow, which will affect the particle size. At the same time, the collision probability of particulate matter is significantly related to the airflow field (respiratory mode). Therefore, it is of great significance to predict the deposition law of particulate matter with different particle sizes and different physical and chemical properties under different respiratory modes. Summary of the Invention

[0003] In view of the above problems, the present invention proposes a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and a construction method thereof, specifically including:

[0004] Obtain the image data, particle properties, fluid flow pattern type, and respiratory tidal volume of the animal's respiratory tract;

[0005] Perform three-dimensional reconstruction on the respiratory tract area through the image data to obtain a respiratory tract model;

[0006] Conduct force analysis on the particles based on the fluid flow pattern type and particle properties to construct an air flow model and a particle motion tracking model;

[0007] Calculate the deformation of the respiratory tract model based on the respiratory tidal volume to construct a respiratory tract deformation model;

[0008] Combine the respiratory tract model, air flow model, particle motion tracking model, and respiratory tract deformation model to obtain a numerical model of the heterogeneous particle deposition distribution in the animal's respiratory tract.

[0009] Determine the force parameters through the force analysis. The particles generate a particle momentum equation based on the force parameters, and a particle motion tracking model is constructed based on the particle momentum equation; the force parameters include one or more of the following: drag force, gravitational force, Brownian force, pressure gradient, virtual mass force, lift force, Basset force.

[0010] The construction of the particle motion tracking model also includes particle collision analysis. Generate a particle collision agglomeration equation through the particle collision, and construct a particle motion tracking model through the particle collision agglomeration equation and the particle momentum equation;

[0011] The deformation of the respiratory tract is calculated by calculating the expansion ratio of the respiratory tract based on the respiratory tidal volume, and then calculated using the expansion ratio and the directional displacement of each plane during the breathing process.

[0012] Optionally, the formula for the normal velocity of the plane of the respiratory tract deformation is expressed as:

[0013]

[0014] Where, i , j , k respectively represent x , y , z the numbers of each plane perpendicular to this direction in the l ( i or j or k ) represents l ( i ) or l ( j ) or l ( k ) the product of the directional displacement relative to the center of the alveolar top surface (red plane) and the expansion ratio.

[0015] The numerical model for heterogeneous particle deposition distribution in the animal respiratory tract further includes model calibration. Animal inhalation experiment data is obtained and input into the numerical model for heterogeneous particle deposition distribution in the animal respiratory tract for data calibration to obtain a calibrated numerical model for heterogeneous particle deposition distribution in the entire animal respiratory tract.

[0016] The three-dimensional reconstruction of the respiratory tract region further includes the reconstruction of the alveolar region. Tissue section data is obtained;

[0017] Based on the tissue section data, three-dimensional reconstruction of the alveolar region is performed to obtain an alveolar region model;

[0018] The imaging data is used to perform three-dimensional reconstruction of the non-alveolar region of the respiratory tract to obtain a non-alveolar region model;

[0019] The alveolar region model and the non-alveolar region model are spliced to obtain a whole respiratory tract model; the respiratory tract deformation amount of the whole respiratory tract model is calculated through the respiratory tidal volume to construct a whole respiratory tract deformation model;

[0020] The whole respiratory tract model, the particle motion model, and the whole respiratory tract deformation model are combined to obtain a numerical model for heterogeneous particle deposition distribution in the whole animal respiratory tract.

[0021] The purpose of the present invention is to provide a computer program product with a computer program or instruction thereon, including: the computer program or instruction is executed by a processor to implement the above-mentioned numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method.

[0022] The purpose of the present invention is to provide a computer device, which includes a memory, a processor, and a computer program or instruction stored on the memory. The computer program or instruction is executed by the processor to implement the above-mentioned numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method. The purpose of the present invention is to provide a method for predicting particle deposition based on a numerical model of particle deposition distribution, specifically including:

[0023] Respiratory parameters, deformation parameters, particle collision parameters, and particle attribute parameters are obtained;

[0024] According to the above-mentioned numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method, a numerical model for heterogeneous particle deposition distribution in the respiratory tract or a numerical model for heterogeneous particle deposition distribution in the whole respiratory tract is obtained;

[0025] Input the respiratory parameters, deformation parameters, particle collision parameters, and particle property parameters into the numerical model of heterogeneous particle deposition distribution in the respiratory tract or the numerical model of heterogeneous particle deposition distribution in the entire respiratory tract to obtain the prediction results of the deposition distribution of heterogeneous particles; the prediction results include the deposition rate of heterogeneous particles and the deposition distribution of heterogeneous particles.

[0026] Optionally, the deposition rate of heterogeneous particles includes the regional deposition rate and the total deposition rate.

[0027] Optionally, the region includes one or more of the following: pharynx, trachea, left bronchus, right bronchus, left bronchiole, right bronchiole, left alveolus, right alveolus.

[0028] Optionally, the method further includes calculating the particle deposition law, and calculating the deposition law of heterogeneous particles according to the regional deposition rate and the total deposition rate.

[0029] The object of the present invention is to provide a computer program product, on which there is a computer program or instruction, including: the computer program or instruction is executed by a processor to implement the above-mentioned particle deposition prediction method based on the numerical model of particle deposition distribution.

[0030] The object of the present invention is to provide a computer device, which includes a memory, a processor, and a computer program or instruction stored on the memory, and the computer program or instruction is executed by the processor to implement the above-mentioned particle deposition prediction method based on the numerical model of particle deposition distribution.

[0031] Advantages of the present invention:

[0032] 1. A numerical model of the deposition distribution of heterogeneous particles is proposed, in which the force analysis of the motion state of heterogeneous particles in the respiratory tract is carried out, including drag force, gravity, Brownian force, pressure gradient, virtual mass force, lift force, and Basset force. Compared with the existing particle deposition models, more influencing factors in the particle motion process are included in the present invention, and the force process is more complex, so it is more in line with the motion state of particles in the actual respiratory tract and improves the accuracy of particle motion.

[0033] 2. In addition to increasing the influence analysis of force on the particle motion state, the collision analysis of the particle motion process is also included, including Brownian collision and inertial collision, which is used to improve the authenticity of the particle motion state and further improve the accuracy of the research on the particle deposition motion mechanism.

[0034] 3. After the particle deposition model is constructed, it also includes model correction. The constructed deposition model is corrected by the animal inhalation experiment data to improve the accuracy of the deposition model.

[0035] 4. Construct a geometric model of the entire respiratory tract of the animal. After geometrically constructing the acinar area and the non-acinar area, the geometric models of the two parts are merged to obtain a geometric model of the entire respiratory tract. This helps to explore the movement process and deposition mechanism of particles in the entire respiratory tract. Compared with the existing segmental respiratory tract deposition model, it is more comprehensive and more in line with the actual respiratory tract model.

[0036] 5. The geometric model of the acinar area in the respiratory tract is constructed using the dense tiling geometric model method. This method constructs an idealized acinar area based on animal tissue slice data, reduces the model error, improves the model's authenticity, and provides a good basic environment for subsequent sedimentation motion simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the 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 work.

[0038] Figure 1 A schematic diagram of a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and a method for constructing the same, provided in an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of a numerical model construction system for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals provided by an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of a device for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal, provided in an embodiment of the present invention;

[0041] Figure 4 A schematic diagram of three-dimensional reconstruction of a non-acinar area provided by an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of three-dimensional reconstruction of the acinar region provided by an embodiment of the present invention;

[0043] Figure 6 The process of constructing the full respiratory tract geometric model provided by the embodiment of the present invention;

[0044] Figure 7 A geometric diagram illustrating respiratory deformation provided by an embodiment of the present invention;

[0045] Figure 8 Schematic diagram of the deposition model provided in an embodiment of the present invention for predicting the deposition mechanism. DETAILED DESCRIPTION

[0046] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0047] In some processes described in the specification and claims of the present invention and the above-mentioned accompanying drawings, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit that "first" and "second" are of different types.

[0048] Figure 1 A schematic diagram of a numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and a method for constructing the same provided by an embodiment of the present invention specifically includes:

[0049] S1. Obtain the imaging data, particle properties, fluid flow regime type, and respiratory tidal volume of the animal respiratory tract;

[0050] In one embodiment, the imaging data includes one or more of the following: CT, MRI, ultrasound, X-ray, radionuclide imaging.

[0051] In one embodiment, the fluid flow regime includes laminar flow and turbulent flow; laminar flow is a flow state of a fluid, and its characteristic is that fluid particles flow along smooth layers, and each layer of fluid flows smoothly past the adjacent layer, with little or no mixing. In the laminar flow state, the liquid particles do not interfere with each other, and the flow of the liquid is linear or laminar, parallel to the axis of the pipeline. Turbulent flow is another fluid flow state, and its characteristic is that the movement of liquid particles is chaotic. In addition to the movement parallel to the axis of the pipeline, there is also a violent lateral movement. In the turbulent flow state, the liquid particles will mix with each other, and the flow state is relatively complex.

[0052] In one embodiment, the animals include one or more of the following: guinea pigs, mice, rats, rabbits.

[0053] S2. Perform three-dimensional reconstruction on the respiratory tract area through the imaging data to obtain a respiratory tract model;

[0054] In one embodiment, the respiratory tract area does not include the alveolar area.

[0055] In one embodiment, the process of three-dimensional reconstruction of the non-acinar region is as follows: obtaining CT images; performing airway segmentation on the CT images to obtain segmented airway data, and performing three-dimensional reconstruction based on the segmented airway data to obtain the non-acinar region, wherein the airway segmentation is performed by threshold segmentation or segmentation network segmentation.

[0056] In one embodiment, the threshold segmentation obtains airway data by removing image regions greater than a preset threshold.

[0057] In one embodiment, the segmentation network includes one or more of the following: FCN, Unet, Unet++, SegNet, RefineNet, Mask R-CNN, DSN, PSPNet, ReSeg, DeepMask.

[0058] The image data performs three-dimensional reconstruction on the non-acinar region of the respiratory tract to obtain a non-acinar region model;

[0059] In another embodiment, the three-dimensional reconstruction of the respiratory tract region further includes the reconstruction of the acinar region, and tissue section data is obtained;

[0060] Based on the tissue section data, three-dimensional reconstruction is performed on the acinar region to obtain an acinar region model;

[0061] In one embodiment, the process of three-dimensional reconstruction of the acinar region is as follows:

[0062] First step, obtaining tissue section data;

[0063] Second step, calculating airway size data, airway length data, and average alveolar size data based on the tissue section data;

[0064] Third step, performing alveolar close packing through the airway size data and the average alveolar size data to obtain two-dimensional close packing data;

[0065] Fourth step, performing three-dimensional stretching based on the two-dimensional close packing data and the airway length data to obtain an acinar region model.

[0066] In one embodiment, the non-acinar region model and the acinar region model are spliced to obtain a whole respiratory tract geometric model; the respiratory tract deformation amount of the whole respiratory tract model is calculated through the respiratory tidal volume to construct a whole respiratory tract deformation model; the whole respiratory tract geometric model, the particle motion model, and the whole respiratory tract deformation model are combined to obtain an animal whole respiratory tract heterogeneous particle deposition numerical model.

[0067] In a specific embodiment, the respiratory tract of a guinea pig is reconstructed. Among them, the process of collecting CT slices of the guinea pig respiratory system and reconstructing the three-dimensional respiratory tract geometric model of the non-acinar region (as Figure 4 shown) is as follows:

[0068] Guinea pigs weighing 320 ± 10 g were intraperitoneally injected with 0.25% sodium pentobarbital (20 mg / kg) to anesthetize and sedate them. The anesthetized guinea pigs were placed on a Bruker small animal micro-CT animal bed and scanned with a 360° rotation at a rotation step of 0.4° (resolution 6 μm). After the scanning was completed, three-dimensional reconstruction was performed on the scanned slices to obtain three-dimensional CT tomographic slices of the respiratory system. Subsequently, the 3Dslicer software was used to segment and reconstruct the airway of the three-dimensional CT slices to obtain a three-dimensional respiratory tract geometric model of the non-acinar region. The specific reconstruction process was to select the HU value according to the difference in the radioactive absorption intensity of different substances. Among them, the airway had the weakest absorption ability, and the HU value was less than 0, which was black on the CT slice. After the software automatically completed the circled airway area, the default algorithm of 3Dslicer was used to reconstruct the three-dimensional respiratory tract of the non-acinar region.

[0069] In a specific embodiment, the collection of paraffin-embedded sections of the guinea pig respiratory system and the reconstruction of the three-dimensional close-packed geometric model of the idealized acinar region of the respiratory tract (as Figure 5 shown) are as follows:

[0070] Guinea pigs weighing 320 ± 10 g were intraperitoneally injected with 1% sodium pentobarbital (70 mg / kg) to be sacrificed. They were dissected, and the lungs were taken and fixed in formalin solution, followed by paraffin embedding and tissue sectioning. Scanning imaging was performed using a Pannoramic MIDI slide scanner, and finally, the 3DHISTECH software was used to count the sizes of the airways and alveoli in the acinar region. And the UNIGRAPHICS software was used to construct a three-dimensional geometric model of the acinar region. The specific reconstruction process was to draw a two-dimensional sketch of the close-packed acini according to the airway size and the average size of the alveoli, and then use the stretching function to reconstruct the three-dimensional close-packed acinar geometric model.

[0071] In a specific embodiment, the ANSYS Spaceclaim software was used to splice the three-dimensional geometric model of the non-acinar region respiratory tract and the three-dimensional geometric model of the acinar region respiratory tract to obtain a three-dimensional geometric model of the whole respiratory tract of the guinea pig. The specific splicing method was to first place the two models in the same file, and then use the moving function to align the positions of the two models. When the alignment was completed, the fusion function was used to splice the two models to obtain a three-dimensional geometric model of the whole respiratory tract of the guinea pig, as Figure 6 shown.

[0072] S3. Construct an air flow model and a particle motion tracking model by analyzing the forces on the particles according to the type of fluid flow state and particle properties;

[0073] In one embodiment, the construction of the air flow model further includes air flow analysis, generating an air flow continuity equation and a momentum equation through the air flow, and constructing an air flow model.

[0074] In one embodiment, the airflow continuity equation and momentum equation are expressed as:

[0075]

[0076]

[0077] Wherein, represents the fluid velocity, represents the air density, is the airflow pressure, is the kinematic viscosity.

[0078] In one embodiment, the force parameters are determined through the force analysis, and the particles generate a particle momentum equation based on the force parameters and construct a particle motion tracking model based on the particle momentum equation.

[0079] In one embodiment, the force parameters include one or more of the following: drag force, gravity, Brownian force, pressure gradient, virtual mass force, lift force, Basset force.

[0080] In one embodiment, the particle momentum equation is expressed as:

[0081]

[0082] Wherein, u represents the fluid velocity, t represents time, m represents mass, F represents force.

[0083] In one embodiment, the construction of the particle motion tracking model further includes particle collision analysis. The particle collision generates a particle collision agglomeration equation, and the particle motion tracking model is constructed through the particle collision agglomeration equation and the particle momentum equation.

[0084] In one embodiment, the particle collisions include Brownian collisions and inertial collisions, and the particle collision equation is expressed as:

[0085]

[0086]

[0087] Where P represents the collision probability, represents the mass of "1" particle at time t, represents the particle radius, represents the particle density, represents the velocity difference between two particles, , represents the distance between two particles.

[0088] In one embodiment, the construction of the particle motion tracking model also includes airflow analysis and particle collision analysis. The airflow continuity equation and momentum equation are generated through the airflow, and the particle collision agglomeration equation is generated through the particle collision. The particle motion tracking model is constructed through the particle collision agglomeration equation, the airflow continuity equation, the airflow momentum equation, and the particle momentum equation.

[0089] In a specific embodiment, the process of constructing the airflow equation is as follows:

[0090] Considering that the airflow in the guinea pig's respiratory tract is laminar and incompressible, the continuity equation and momentum equation are:

[0091] Continuity equation:

[0092]

[0093] Momentum equation:

[0094]

[0095] in u represents the fluid velocity, ρ represents the air density, p is the air flow pressure, v is the kinematic viscosity.

[0096] In a specific embodiment, the particle momentum equation is constructed as follows:

[0097] Since the respiratory movement of the lungs is rhythmic breathing, particles are very likely to collide and agglomerate due to the oscillating airflow. Therefore, the particle momentum equation under rhythmic breathing conditions is obtained by analyzing the forces acting on micro-nano particles:

[0098]

[0099] in, drag represents the drag force, gravitation represents gravity, Brown represents the Brownian force, pressure gradient represents the pressure gradient force, virtual mass represents the virtual mass force, and lift represents the lift force.

[0100] The expressions and magnitudes of the forces are:

[0101]

[0102] in, is the aerodynamic viscosity, is the particle mass, is the particle velocity, is the particle density, is the particle size, is the Cunningham correction factor, is the acceleration due to gravity, is a random number, represents the Boltzmann constant, is the Kelvin temperature, represents the particle radius, is the time step, is the virtual mass coefficient, represents the relaxation time.

[0103] In a specific embodiment, the particle collision agglomeration equation is constructed as follows:

[0104] Considering that the main reasons for the collision of micro-nano particles under the condition of rhythmic breathing laminar flow are Brownian collision and inertial collision, the particle collision agglomeration equation is

[0105]

[0106]

[0107]

[0108]

[0109] where represents the collision probability, represents the mass of particle "1" at time t, represents the particle radius, represents the particle density, represents the velocity difference between two particles, , represents the distance between two particles.

[0110] S4. Calculate the deformation of the respiratory tract of the respiratory tract model through the tidal volume of breathing to construct a respiratory tract deformation model;

[0111] In one embodiment, the deformation of the respiratory tract is calculated by calculating the expansion ratio of the respiratory tract through the tidal volume of breathing, and then calculating it using the expansion ratio and the directional displacement of each plane during breathing.

[0112] In a specific embodiment, the respiratory tract deformation equation is constructed as follows:

[0113] Determine the expansion amplitude of the respiratory tract according to the tidal volume of breathing, which is represented by the expansion ratio Since the breathing waveform of guinea pigs is approximately sinusoidal breathing, the normal velocity equation of each plane movement of the alveoli is:

[0114]

[0115] Among them, i , j , k respectively represent x , y , z the numbers of each plane perpendicular to this direction in the l ( i or j or k ) represents l ( i ) or l ( j ) or l ( k ) the product of the directional displacement relative to the center of the alveolar top surface (red plane) and the expansion ratio, as shown by the red plane in Figure 7 .

[0116] S5. Combine the respiratory tract geometric model, the airflow model, the particle motion tracking model, and the respiratory tract deformation model to obtain a numerical model of the heterogeneous particle deposition distribution in the animal respiratory tract.

[0117] In one embodiment, combine the whole respiratory tract geometric model, the airflow model, the particle motion tracking model, and the respiratory deformation model to obtain a numerical model of the heterogeneous particle deposition distribution in the animal respiratory tract. In one embodiment, the heterogeneous particle deposition model of the animal respiratory tract further includes model correction. Obtain animal inhalation experiment data, and input the animal inhalation experiment data into the heterogeneous particle deposition model of the animal respiratory tract for data correction to obtain a corrected numerical model of the heterogeneous particle deposition distribution in the animal respiratory tract.

[0118] In a specific embodiment, first select the airflow equation according to the fluid flow regime, then perform a force analysis on the particle motion process to determine the final forces on the particles (such as gravity, drag force, Brownian force, etc.) to construct the particle momentum equation and the particle collision and aggregation equation, and determine the expansion amplitude of the alveoli (dynamic mesh parameters) according to the respiratory tidal volume to construct the respiratory tract deformation equation. Finally, use computational fluid dynamics software to select the airflow physical field, the particle collision and aggregation physical field, the particle momentum equation, and the geometric deformation physical field according to the determined and constructed equations, and combine the three-dimensional whole respiratory tract geometric model of guinea pigs to obtain a three-dimensional high-humidity whole respiratory tract particulate matter deposition numerical model of guinea pigs during rhythmic breathing. And use the existing animal inhalation experiment results to correct the accuracy of the numerical model.

[0119] In a specific embodiment, for the construction process, it mainly includes using CT slices and paraffin-embedded slices to construct a three-dimensional respiratory tract geometric model of the real non-acinar region of guinea pigs and an idealized three-dimensional close-packed geometric model of the acinar region of the respiratory tract respectively. Subsequently, a three-dimensional whole respiratory tract geometric model of guinea pigs is constructed based on the above segmented models. Finally, a three-dimensional high-humidity whole respiratory tract particle deposition numerical model of guinea pig rhythmic breathing is constructed based on the three-dimensional whole respiratory tract geometric model and mathematical theory analysis.

[0120] The disclosed embodiments of the present invention also provide a computer program product or system, including a computer program, which when executed by a processor implements the steps of the above numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method.

[0121] Figure 2 A schematic diagram of a numerical model construction system for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract provided by an embodiment of the present invention specifically includes:

[0122] An acquisition unit: acquiring image data of the animal respiratory tract, particle properties, fluid flow state type, and respiratory tidal volume;

[0123] A reconstruction unit: performing three-dimensional reconstruction on the respiratory tract region through the image data to obtain a respiratory tract model;

[0124] A motion unit: constructing an air flow model and a particle motion tracking model by analyzing the forces on the particles through the fluid flow state type and particle properties;

[0125] A deformation unit: constructing a respiratory tract deformation model by calculating the respiratory tract deformation amount of the respiratory tract model through the respiratory tidal volume;

[0126] A deposition unit: combining the respiratory tract geometric model, the particle motion tracking model, and the respiratory tract deformation model to obtain a numerical model of the deposition distribution of heterogeneous particles in the animal respiratory tract.

[0127] Figure 3 A schematic diagram of a numerical model construction device for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract provided by an embodiment of the present invention specifically includes:

[0128] A memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the above numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method.

[0129] The disclosed embodiments of the present invention also provide a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement any one of the above numerical models for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method.

[0130] An embodiment of the present invention provides a method for predicting particle deposition based on a numerical model of particle deposition distribution, specifically including:

[0131] Obtain respiratory parameters, deformation parameters, particle collision parameters, and particle property parameters;

[0132] According to the above-mentioned numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method, obtain a respiratory tract heterogeneous particle deposition model or a whole respiratory tract heterogeneous particle deposition model;

[0133] Input the respiratory parameters, deformation parameters, particle collision parameters, and particle property parameters into the respiratory tract heterogeneous particle deposition model or the whole respiratory tract heterogeneous particle deposition model to obtain the deposition prediction results of heterogeneous particles; the prediction results include the deposition rate of heterogeneous particles and the deposition distribution of heterogeneous particles;

[0134] In one embodiment, the deposition rate of heterogeneous particles includes regional deposition rate and total deposition rate.

[0135] In one embodiment, the region includes one or more of the following: pharynx, trachea, left bronchus, right bronchus, left bronchiole, right bronchiole, left alveolus, right alveolus.

[0136] In one embodiment, the method further includes calculating the particle deposition law, and calculating the deposition law of heterogeneous particles according to the regional deposition rate and the total deposition rate.

[0137] In one embodiment, the method further includes analyzing the particle deposition mechanism, recording the movement trajectory of the particle during the deposition process, marking the deposition mechanism of each particle by comparing with the particle deposition mechanism standard, and obtaining the deposition mechanism of heterogeneous particles in different regions based on the movement trajectory of the particle and the deposition mechanism of the particle.

[0138] In a specific embodiment, for the prediction process, it mainly includes inputting and integrating respiratory parameters (frequency, tidal volume) and particle properties (particle size, density, hydrophilicity / hydrophobicity) into the particle deposition numerical model, predicting the particle deposition law through the model, and tracking the particle trajectory to obtain the particle deposition mechanism.

[0139] In a specific embodiment, the particle deposition prediction process is as follows:

[0140] I Particle deposition numerical model integration:

[0141] Set the input respiratory parameters, particle properties and other parameters in the particle deposition numerical model. Calculate the air flow velocity at the outlet and the expansion amplitude of the alveoli according to the breathing frequency and tidal volume. Set the boundary condition at the outlet as the air flow velocity in the physical field of air flow, and set the boundary condition at the inlet as the pressure of 0 Pa. Set the deformation parameters of the alveoli in the physical field of geometric deformation. Set the particle property parameters and the number of released particles in the physical field of particle deposition, and set the particle collision parameters at the same time.

[0142] II Prediction of particle deposition law:

[0143] Use computational fluid dynamics software to perform calculations on the particle deposition numerical model to predict the particle deposition law. After the calculation is completed, use the computational fluid dynamics software to post-process the particle deposition amounts in different regions and the total amount of deposited particles in the entire respiratory tract. Subsequently, perform calculations on the particle deposition law, with the total deposition rate DF total and the regional deposition rate DF i represented, where i represents the nasopharynx, trachea, left bronchus, right bronchus, left bronchiole, right bronchiole, left alveolus, and right alveolus.

[0144]

[0145]

[0146] III Tracking of particle movement trajectories and revealing of deposition mechanisms:

[0147] After the calculation is completed, track the movement trajectories during the particle deposition process, as Figure 8 shown. Mark the deposition mechanism of each particle by comparing with the standard of the particle deposition mechanism, and then analyze the deposition mechanisms of heterogeneous particle sizes in different regions.

[0148] The disclosed embodiments of the present invention also provide a computer program product or system, including a computer program, which implements the steps of the above-mentioned particle deposition prediction method based on the particle deposition distribution numerical model when executed by a processor.

[0149] A particle deposition prediction system based on a particle deposition distribution numerical model provided by an embodiment of the present invention specifically includes:

[0150] Parameter unit: Obtain respiratory parameters, deformation parameters, particle collision parameters, and particle property parameters;

[0151] Model unit: Obtain the numerical model of heterogeneous particle deposition distribution in the respiratory tract or the numerical model of heterogeneous particle deposition distribution in the entire respiratory tract according to the above-mentioned numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method;

[0152] Prediction unit: input the respiratory parameters, deformation parameters, particle collision parameters, and particle property parameters into the numerical model of heterogeneous particle deposition distribution in the respiratory tract or the numerical model of heterogeneous particle deposition distribution in the entire respiratory tract to obtain the deposition prediction results of heterogeneous particles; the prediction results include the deposition rate of heterogeneous particles and the deposition distribution of heterogeneous particles.

[0153] A particle deposition prediction device based on a numerical model of particle deposition distribution provided by an embodiment of the present invention specifically includes:

[0154] A memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, any one of the above-mentioned particle deposition prediction methods based on the numerical model of particle deposition distribution.

[0155] An embodiment of the present invention also provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any one of the above-mentioned particle deposition prediction methods based on the numerical model of particle deposition distribution.

[0156] An embodiment of the present invention provides a method for constructing a geometric model of an animal's entire respiratory tract, including:

[0157] Obtain imaging data and animal tissue section data;

[0158] Perform three-dimensional reconstruction of the non-acinar region of the animal's respiratory tract based on the imaging data to obtain a non-acinar region model;

[0159] Perform three-dimensional reconstruction of the acinar region of the animal's respiratory tract based on the animal tissue section data to obtain an acinar region model;

[0160] Stitch the non-acinar region model and the acinar region model to obtain a geometric model of the animal's entire respiratory tract.

[0161] In one embodiment, the acinar region is densely paved with alveoli through the airway size and alveolar size in the tissue section data to obtain a two-dimensional dense paving model, and then three-dimensional stretching is performed based on the two-dimensional dense paving model to obtain a three-dimensional acinar region model.

[0162] In another embodiment, the acinar region generates three-dimensional Thiessen polygons through random point clouds, then constructs acinar paths within the three-dimensional Thiessen polygons using an optimization algorithm, and performs smoothing processing using quadrilateral surface reconstruction and subdivision modeling to obtain an idealized polyhedral acinar model at the end of the respiratory tract; among them, before the smoothing processing, delete the polygon faces passed on the acinar paths and connect the edges of the two adjacent three-dimensional Thiessen polygons to obtain a flow channel connecting each acinus, perform geometric repair, and create an entrance for the idealized acinar model.

[0163] The disclosed embodiments of the present invention also provide a computer program product or system, including a computer program which, when executed by a processor, implements the steps of the above-mentioned method for constructing an animal whole respiratory tract geometric model.

[0164] An animal whole respiratory tract geometric model construction system provided by an embodiment of the present invention specifically includes:

[0165] Data acquisition unit: acquiring imaging data and animal tissue section data;

[0166] Non-acinar unit: performing three-dimensional reconstruction on the non-acinar region of the animal respiratory tract based on the imaging data to obtain a non-acinar region model;

[0167] Acinar unit: performing three-dimensional reconstruction on the acinar region of the animal respiratory tract based on the animal tissue section data to obtain an acinar region model;

[0168] Geometry unit: splicing the non-acinar region model and the acinar region model to obtain an animal whole respiratory tract geometric model;

[0169] An animal whole respiratory tract geometric model construction device provided by an embodiment of the present invention specifically includes:

[0170] A memory and a processor; the memory is used for storing program instructions; the processor is used for calling the program instructions, and the program instructions are executed to perform any one of the above-mentioned methods for constructing an animal whole respiratory tract geometric model.

[0171] The disclosed embodiments of the present invention also provide a computer-readable storage medium storing a computer program which, when executed by a processor, performs any one of the above-mentioned methods for constructing an animal whole respiratory tract geometric model.

[0172] The verification results of this verification embodiment show that adapting the assigned fixed weight can improve the performance of this method compared to the default settings. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated herein. In several embodiments provided by this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in each embodiment of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk, or optical disc, etc.

[0173] Those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned medium storage can be read-only memory, magnetic disk, or optical disc, etc.

[0174] The above has introduced in detail a computer device provided by the present invention. For those of ordinary skill in the art, according to the idea of the embodiments of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals, characterized in that, Comprising: Obtaining image data, particle properties, fluid flow state types, and respiratory tidal volumes of an animal's respiratory tract; Performing three-dimensional reconstruction on the respiratory tract region through the image data to obtain a respiratory tract model; Constructing an air flow model and a particle motion tracking model through force analysis of particles based on the fluid flow state type and particle properties; The construction of the air flow model further includes air flow analysis, generating an air flow continuity equation and a momentum equation through the air flow, and constructing an air flow model; Determining force parameters through the force analysis, generating a particle momentum equation for the particles based on the force parameters, and the construction of the particle motion tracking model further includes particle collision analysis, generating a particle collision agglomeration equation through the particle collision, and constructing a particle motion tracking model through the particle collision agglomeration equation and the particle momentum equation; Collisions include Brownian collisions and inertial collisions; Calculating the deformation amount of the respiratory tract of the respiratory tract model through the respiratory tidal volume, and constructing a respiratory tract deformation model; Combining the respiratory tract model, the air flow model, the particle motion tracking model, and the respiratory tract deformation model to obtain a numerical model of the heterogeneous particle deposition distribution in the animal's respiratory tract; The respiratory tract deformation is calculated by calculating the expansion ratio of the respiratory tract through the respiratory tidal volume, and then using the expansion ratio and the directional displacement of each plane during breathing for calculation; The formula for the normal velocity of the plane of the respiratory tract deformation is expressed as: Among them, i , j , k respectively represent x , y , z the numbers of each plane perpendicular to this direction in the x , y , z directions, l ( i or j or k )represents l ( i )or l ( j )or l ( k )the product of the direction displacement relative to the center of the top surface of the acinus and the expansion ratio.

2. The method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals according to claim 1, characterized in that, The force parameters include one or more of the following: drag force, gravitational force, Brownian force, pressure gradient, virtual mass force, lift force, Basset force.

3. The method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals according to claim 1, characterized in that, The animal respiratory tract heterogeneous particle deposition model further includes model calibration, obtaining animal inhalation experimental data, and inputting the animal inhalation experimental data into the animal respiratory tract heterogeneous particle deposition model for data calibration to obtain a calibrated animal respiratory tract heterogeneous particle deposition model.

4. The method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals according to claim 1, characterized in that, The three-dimensional reconstruction of the respiratory tract region further includes the reconstruction of the alveolar region, obtaining tissue section data; Performing three-dimensional reconstruction on the alveolar region based on the tissue section data to obtain an alveolar region model; Performing three-dimensional reconstruction on the non-alveolar region of the respiratory tract through the image data to obtain a non-alveolar region model; Splicing the alveolar region model and the non-alveolar region model to obtain a full respiratory tract geometric model; Calculating the deformation amount of the respiratory tract of the full respiratory tract geometric model through the respiratory tidal volume to construct a full respiratory tract deformation model; Combining the full respiratory tract geometric model, the particle motion tracking model, and the full respiratory tract deformation model to obtain a numerical model of the heterogeneous particle deposition distribution in the animal's full respiratory tract; 5. A system for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals, characterized in that, The system is obtained based on the method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in an animal's respiratory tract according to any one of claims 1-4.

6. A particle deposition prediction method based on a numerical model of particle deposition distribution, characterized in that, Comprising: Obtaining respiratory parameters, deformation parameters, particle collision parameters, and particle property parameters; Obtaining a numerical model of the heterogeneous particle deposition distribution in the respiratory tract or a numerical model of the heterogeneous particle deposition distribution in the full respiratory tract according to the method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in an animal's respiratory tract according to any one of claims 1-4; Input the respiratory parameters, deformation parameters, particle collision parameters, and particle property parameters into a numerical model of heterogeneous particle deposition distribution in the respiratory tract or a numerical model of heterogeneous particle deposition distribution in the entire respiratory tract to obtain a predicted result of the deposition distribution of heterogeneous particles; the predicted result includes the deposition rate of heterogeneous particles and the deposition distribution of heterogeneous particles.

7. A computer program product having a computer program or instructions thereon, characterized in that, including: The computer program or instruction is executed by a processor to implement the method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal according to any one of claims 1-4, or to execute the method for predicting particle deposition based on a numerical model of particle deposition distribution according to claim 6.

8. A computer device, comprising a memory, a processor, and a computer program or instruction stored on the memory, characterized in that, The computer program or instruction is executed by the processor to implement the method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal according to any one of claims 1-4, or to execute the method for predicting particle deposition based on a numerical model of particle deposition distribution according to claim 6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instruction is executed by a processor to implement the method for constructing a numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal according to any one of claims 1-4, or to execute the method for predicting particle deposition based on a numerical model of particle deposition distribution according to claim 6.

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