Numerical model for predicting deposition distribution of heterogeneous particles in animal respiratory tract and construction method thereof

Abstract

The invention relates to the field of intelligent medical treatment, in particular to a numerical model for predicting deposition distribution of heterogeneous particles in animal respiratory tracts and a construction method of the numerical model. Comprising the following steps: acquiring image data, particle attributes, fluid flow state types and respiratory tidal volume of animal respiratory tracts; performing three-dimensional reconstruction on the respiratory tract area through the image data to obtain a respiratory tract model; analyzing particle stress through fluid flow state types and particle attributes to construct a particle motion tracking model; calculating the respiratory tract deformation quantity of the respiratory tract model through the respiratory tidal volume to construct a respiratory tract deformation model; and combining the respiratory tract model, the particle motion tracking model and the respiratory tract deformation model to obtain the animal respiratory tract heterogeneous particle deposition distribution numerical model. The device can simulate deposition distribution and deposition amount of heterogeneous particles in different areas of the respiratory tract, and has good application value.

Description

Technical Field

[0001] The present application relates to the field of intelligent medicine, and specifically 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 disease 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 dose of drug deposition in the lungs and the local deposition dose of the target lung tissue. Animal in vivo experimental verification is a prerequisite before clinical drugs are marketed. Therefore, it is of great significance to understand the deposition law and mechanism of particles in the animal respiratory tract. However, due to the extremely complex structure of the respiratory tract and its non-transparent tissue, and the influence of existing technology, current animal experimental studies can only obtain the total deposition rate and regional deposition rate of particles in animals, and cannot obtain high-resolution deposition distribution of particles in different regions. Ye et al. used guinea pigs to study the deposition distribution of 2μm polydisperse particle aerosols in the respiratory tract. After observation of paraffin-embedded sections, it was found that most of the 2μm particles were deposited in the lower respiratory tract area. Philip et al. studied the exposure of rats and mice to 0.5μm, 1μm, 3μm, and 5μm radioactive polydisperse aerosols ( 99m Tc), single photon emission computed tomography / X-ray tomography (SPECT / CT) was used to quantitatively analyze the regional deposition rate, and it was found that as the particle size increased, the total respiratory tract deposition rate decreased, the upper respiratory tract deposition rate increased, and the lung deposition rate decreased. However, the aerosol particles generated in the above experiments were all polydisperse (GSD>1.5), and the particle size span was large. For example, the actual range of the 5μm aerosol particle size was 1μm ~ 15μm. Therefore, it is impossible to accurately reflect the movement and deposition law of a single particle size, and it is also impossible to construct a numerical model of particle deposition, and thus it is impossible to obtain the deposition mechanism of particles of heterogeneous particle sizes. In addition, due to the influence of previous technologies, the geometric model of the animal respiratory tract is difficult to reconstruct. Therefore, there are few reports on numerical studies on the distribution of particle deposition in the respiratory tract of small animals. In addition, the respiratory tract is a high-humidity environment. Hydrophilic particles entering the respiratory tract will grow rapidly. As the respiratory airflow flows, particles are likely to collide and agglomerate, which in turn affects the particle size. At the same time, the collision probability of particles is significantly correlated with the airflow field (breathing pattern). Therefore, it is of great significance to predict the deposition patterns of particles of different sizes and different physical and chemical properties under different breathing patterns. 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, which specifically includes: Obtain imaging data of the animal's respiratory tract, particle properties, fluid flow pattern type, and respiratory tidal volume; Performing three-dimensional reconstruction of the respiratory tract region using the image data to obtain a respiratory tract model; The airflow model and particle motion tracking model are constructed by analyzing the particle force through the fluid flow type and particle properties; Calculating the respiratory tract deformation amount of the respiratory tract model by the respiratory tidal volume to construct a respiratory tract deformation model; The respiratory tract model, airflow model, particle motion tracking model, and respiratory tract deformation model are combined to obtain a numerical model of heterogeneous particle deposition distribution in the animal respiratory tract.

[0004] The force parameters are determined by 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, gravity, Brownian force, pressure gradient, virtual mass force, lift, and Bassett force.

[0005] The construction of the particle motion tracking model also includes particle collision analysis, generating a particle collision agglomeration equation through the particle collision, and constructing the particle motion tracking model through the particle collision agglomeration equation and the particle momentum equation; The respiratory deformation is obtained by calculating the expansion ratio of the respiratory tract through the respiratory tidal volume, and then calculating using the expansion ratio and the directional displacement of each plane during the breathing process.

[0006] Optionally, the plane normal velocity formula of the respiratory tract deformation is expressed as:

[0007] in, i , j , k Respectively represent x , y , z The number of each plane perpendicular to the direction, l ( i or j or k )represent l ( i )or l ( j )or l ( k ) is the product of the directional displacement relative to the center of the alveolar apex (red plane) and the expansion ratio.

[0008] The numerical model of heterogeneous particle deposition distribution in the animal respiratory tract also includes model correction, which obtains animal inhalation experimental data, and inputs the animal inhalation experimental data into the numerical model of heterogeneous particle deposition distribution in the animal respiratory tract for data correction to obtain a corrected numerical model of heterogeneous particle deposition distribution in the entire animal respiratory tract.

[0009] The three-dimensional reconstruction of the respiratory tract region also includes reconstruction of the acinus region to obtain tissue section data; Performing three-dimensional reconstruction of the acinar region based on the tissue section data to obtain an acinar region model; The image data is used to perform three-dimensional reconstruction of the non-acinar region of the respiratory tract to obtain a non-acinar region model; The acinar region model and the non-acinar region model are spliced ​​together to obtain a full respiratory tract model; the respiratory tract deformation amount of the full respiratory tract model is calculated by respiratory tidal volume to construct a full respiratory tract deformation model; The whole respiratory tract model, the particle motion model and the whole respiratory tract deformation model are combined to obtain a numerical model of heterogeneous particle deposition distribution in the whole respiratory tract of animals.

[0010] The object of the present invention is to provide a computer program product having a computer program or instructions thereon, including: the computer program or instructions are executed by a processor to implement the above-mentioned numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and its construction method.

[0011] 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 in the memory, wherein 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 respiratory tract of animals and the method for constructing the same. The object of the present invention is to provide a particle deposition prediction method based on the numerical model of particle deposition distribution, which specifically includes: Obtain breathing parameters, deformation parameters, particle collision parameters, and particle attribute parameters; According to the above numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and its construction method, a numerical model for the deposition distribution of heterogeneous particles in the respiratory tract or a numerical model for the deposition distribution of heterogeneous particles in the entire respiratory tract is obtained; Inputting the breathing parameters, deformation parameters, particle collision parameters, and particle attribute 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 prediction result of the deposition distribution of heterogeneous particles; the prediction result includes a heterogeneous particle deposition rate and a heterogeneous particle deposition distribution; Optionally, the heterogeneous particle deposition rate includes a regional deposition rate and a total deposition rate; Optionally, the region includes one or more of the following: pharynx, trachea, left bronchus, right bronchus, left bronchiole, right bronchiole, left acinus, right acinus; Optionally, the method further comprises calculating a particle deposition law, and calculating the deposition law of the heterogeneous particles according to the regional deposition rate and the total deposition rate.

[0012] The object of the present invention is to provide a computer program product having a computer program or instructions thereon, comprising: the computer program or instructions are executed by a processor to implement the above-mentioned particle deposition prediction method based on a particle deposition distribution numerical model.

[0013] The object of the present invention is to provide a computer device, which includes a memory, a processor and a computer program or instructions stored in the memory, wherein the computer program or instructions are executed by the processor to implement the above-mentioned particle deposition prediction method based on the particle deposition distribution numerical model.

[0014] Advantages of the present invention: 1. A numerical model of the deposition distribution of heterogeneous particles is proposed, in which the motion state of heterogeneous particles in the respiratory tract is analyzed by force, including drag, gravity, Brownian force, pressure gradient, virtual mass force, lift, and Bassett force. Compared with the existing particle deposition model, the present invention incorporates more influencing factors of the particle motion process, and its force process is more complex, which is more in line with the motion state of particles in the actual respiratory tract, thereby improving the accuracy of particle motion.

[0015] 2. In addition to the analysis of the influence of force on the particle motion state, the collision analysis of the particle motion process, including Brownian collision and inertial collision, is also included to improve the authenticity of the particle motion state and thus improve the accuracy of the study of the particle deposition motion mechanism.

[0016] 3. After the particle deposition model is constructed, it also includes model correction. The constructed deposition model is corrected through animal inhalation experimental data to improve the accuracy of the deposition model.

[0017] 4. Construct a geometric model of the animal's entire respiratory tract. 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.

[0018] 5. The geometric model of the alveolar area in the respiratory tract is constructed using the dense geometric model method. This method constructs an idealized alveolar area based on animal tissue section 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

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. 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.

[0020] Figure 1 A numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal and a schematic flow chart of a construction method thereof provided in an embodiment of the present invention; Figure 2 A schematic diagram of a numerical model construction system for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal provided by an embodiment of the present invention; Figure 3 A schematic diagram of a numerical model construction device for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal provided in an embodiment of the present invention; Figure 4 A schematic diagram of three-dimensional reconstruction of a non-acinar area provided in an embodiment of the present invention; Figure 5 A schematic diagram of three-dimensional reconstruction of an acinar region provided by an embodiment of the present invention; Figure 6 The process of constructing the full respiratory tract geometric model provided by the embodiment of the present invention; Figure 7 A geometric diagram describing breathing deformation provided by an embodiment of the present invention; Figure 8 A schematic diagram of a deposition model provided in an embodiment of the present invention for predicting a deposition mechanism. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solutions 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.

[0022] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The sequence numbers of the operations, such as S101, S102, etc., are only used to distinguish between different operations, and the sequence 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 of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0023] Figure 1 A numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal and a schematic diagram of a construction method thereof provided in an embodiment of the present invention specifically include: S1. Obtain the imaging data, particle properties, fluid flow type, and respiratory tidal volume of the animal's respiratory tract; In one embodiment, the image data includes one or more of the following: CT, MRI, ultrasound, X-ray, and nuclear imaging.

[0024] In one embodiment, the fluid flow state includes laminar flow and turbulent flow; laminar flow is a flow state of the fluid, characterized in that the fluid particles flow along smooth layers, and each layer of fluid flows smoothly through the adjacent layers 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 pipe. Turbulent flow is another fluid flow state, characterized in that the movement of the liquid particles is chaotic, and in addition to the movement parallel to the axis of the pipe, there is also violent lateral movement. In the turbulent state, the liquid particles will mix with each other, and the flow state is more complicated.

[0025] In one embodiment, the animal comprises one or more of the following: guinea pig, mouse, rat, rabbit.

[0026] S2. Reconstructing the respiratory tract area in three dimensions using the image data to obtain a respiratory tract model; In one embodiment, the respiratory region does not include an acinar region.

[0027] In one embodiment, the process of three-dimensional reconstruction of the non-acinar area is: acquiring a CT image; performing airway segmentation on the CT image to obtain segmented airway data, and performing three-dimensional reconstruction based on the segmented airway data to obtain the non-acinar area, wherein the airway segmentation is performed by threshold segmentation or segmentation network segmentation.

[0028] In one embodiment, the threshold segmentation obtains airway data by removing image regions larger than a preset threshold.

[0029] 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.

[0030] The image data is used to perform three-dimensional reconstruction of the non-acinar region of the respiratory tract to obtain a non-acinar region model; In another embodiment, the three-dimensional reconstruction of the respiratory region further includes reconstruction of the acinar region to obtain tissue section data; Performing three-dimensional reconstruction of the acinar region based on the tissue section data to obtain an acinar region model; In one embodiment, the three-dimensional reconstruction process of the acinar region is: The first step is to obtain tissue section data; The second step is to calculate the airway size data, airway length data, and average alveolar size data based on the tissue section data; The third step is to perform alveolar tessellation through the airway size data and the average alveolar size data to obtain two-dimensional tessellation data; The fourth step is to obtain the alveolar region model by three-dimensional stretching based on the two-dimensional dense paving data and airway length data.

[0031] In one embodiment, the non-acinar area model and the acinar area model are spliced ​​together to obtain a full respiratory tract geometric model; the respiratory tract deformation amount of the full respiratory tract model is calculated by respiratory tidal volume to construct a full respiratory tract deformation model; the full respiratory tract geometric model, particle motion model, and full respiratory tract deformation model are combined to obtain a numerical model of heterogeneous particle deposition in the animal's full respiratory tract.

[0032] In a specific embodiment, the guinea pig respiratory tract is reconstructed, wherein CT slices of the guinea pig respiratory system are collected and a three-dimensional respiratory tract geometric model of the non-acinar region is reconstructed (e.g. Figure 4 The process is as follows: Guinea pigs weighing 320±10g were anesthetized and sedated by intraperitoneal injection of 0.25% sodium pentobarbital (20mg / kg). The anesthetized guinea pigs were placed on the Bruker small animal micro-CT animal bed and scanned 360° with a rotation step of 0.4° (resolution 6μm). After the scan was completed, the scanned slices were reconstructed in three dimensions to obtain three-dimensional CT tomographic slices of the respiratory system. Subsequently, the 3Dslicer software was used to segment the airways of the three-dimensional CT slices and reconstruct the three-dimensional airway geometric model of the non-acinar region. The specific reconstruction process was to select the HU value according to the difference in radioactive absorption intensity of different substances. The airway had the weakest absorption capacity, the HU value was less than 0, and it was black on the CT slice. After the software automatically circled the airway area, the default algorithm of 3Dslicer was used to reconstruct the three-dimensional airway in the non-acinar region.

[0033] In a specific embodiment, paraffin-embedded sections of the guinea pig respiratory system are collected and a three-dimensional airway tiling geometric model of an idealized acinar region is reconstructed (e.g. Figure 5 The process is as follows: Guinea pigs weighing 320±10g were killed by intraperitoneal injection of 1% sodium pentobarbital (70mg / kg). The lungs were dissected and fixed in formalin solution, then embedded in paraffin and sliced, and scanned using a Pannoramic MIDI slice scanner. Finally, the airway and alveolar sizes in the acinar region were counted using 3DHISTECH software. The three-dimensional geometric model of the acinar region was constructed using UNIGRAPHICS software. The specific reconstruction process was to draw a two-dimensional sketch of the densely packed acinar according to the airway size and the average size of the alveoli, and then use the stretching function to reconstruct the three-dimensional densely packed acinar geometric model.

[0034] In a specific embodiment, ANSYS Spaceclaim software is used to splice the non-acinar region respiratory tract 3D geometric model and the acinar region respiratory tract 3D geometric model to obtain a guinea pig full respiratory tract 3D geometric model. The specific splicing method is to first place the two models in the same file, then use the move function to match the positions of the two models, and after the correspondence is completed, use the fusion function to splice the two models to obtain the guinea pig full respiratory tract 3D geometric model. Figure 6 shown.

[0035] S3, constructing an airflow model and a particle motion tracking model by analyzing the particle force through the fluid flow type and particle properties; In one embodiment, the construction of the airflow model further includes airflow analysis, generating an airflow continuity equation and a momentum equation through the airflow, and constructing the airflow model.

[0036] In one embodiment, the airflow continuity equation and momentum equation are expressed as:

[0037]

[0038] in, represents the fluid velocity, represents the air density, is the air flow pressure, is the kinematic viscosity.

[0039] In one embodiment, force parameters are determined by 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.

[0040] In one embodiment, the force parameters include one or more of the following: drag, gravity, Brownian force, pressure gradient, virtual mass force, lift, and Bassett force.

[0041] In one embodiment, the particle momentum equation is expressed as:

[0042] in, u represents the fluid velocity, t Expressed as time, m Expressed as mass, F Expressing power.

[0043] In one embodiment, the construction of the particle motion tracking model further includes particle collision analysis, through which a particle collision agglomeration equation is generated, and the particle motion tracking model is constructed through the particle collision agglomeration equation and the particle momentum equation.

[0044] In one embodiment, the particle collision includes Brownian collision and inertial collision, and the particle collision equation is expressed as:

[0045]

[0046] Where P 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 the two particles, , Represents the distance between two particles.

[0047] 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 flow, 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.

[0048] In a specific embodiment, the process of constructing the air flow equation is as follows: Considering that the airflow in the guinea pig's respiratory tract is laminar and incompressible, the continuity equation and momentum equation are: Continuity equation:

[0049] Momentum equation:

[0050] in u represents the fluid velocity, ρ represents the air density, p is the air flow pressure, v is the kinematic viscosity.

[0051] In a specific embodiment, the particle momentum equation is constructed as follows: 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 force analysis of micro-nano particles is used to obtain the particle momentum equation under rhythmic breathing conditions:

[0052]

[0053] in, drag represents the drag force, gravitation represents gravity, Brown represents the Brownian force, pressure gradient represents the pressure gradient force, virtual mass represents virtual mass force, and lift represents lift.

[0054] The expressions and magnitudes of the forces are:

[0055]

[0056] 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 coefficient, 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 quality coefficient, Represents relaxation time.

[0057] In a specific embodiment, the particle collision agglomeration equation is constructed as: Considering that the main causes of micro-nanoparticle collisions under rhythmic breathing laminar flow conditions are Brownian collisions and inertial collisions, the particle collision agglomeration equation is:

[0058]

[0059]

[0060]

[0061] in 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 the two particles, , Represents the distance between two particles.

[0062] S4, calculating the respiratory tract deformation amount of the respiratory tract model by the respiratory tidal volume to construct a respiratory tract deformation model; In one embodiment, the airway deformation is obtained by calculating the expansion ratio of the airway through the breathing tidal volume, and then calculating using the expansion ratio and the directional displacement of each plane during breathing.

[0063] In one specific embodiment, the airway deformation equation is constructed as: The expansion ratio is used to determine the expansion amplitude of the airway according to the tidal volume of the breath. Since the breathing waveform of guinea pigs is approximately sinusoidal breathing, the normal velocity equation of each plane movement of the alveoli is:

[0064] in, i , j , k Respectively represent x , y , z The number of each plane perpendicular to the direction, l ( i or j or k )represent l ( i )or l ( j )or l ( k ) is the product of the displacement of the center of the alveolar top surface (red plane) and the expansion ratio. The red plane is as follows Figure 7 shown.

[0065] S5. Combining the respiratory tract geometry model, airflow model, particle motion tracking model, and respiratory tract deformation model to obtain a numerical model of heterogeneous particle deposition distribution in the animal respiratory tract.

[0066] In one embodiment, the whole respiratory tract geometry model, the airflow model, the particle motion tracking model, and the respiratory deformation model are combined to obtain a numerical model of heterogeneous particle deposition distribution in the animal respiratory tract. In one embodiment, the animal respiratory tract heterogeneous particle deposition model also includes model correction, obtaining animal inhalation experimental data, and inputting the animal inhalation experimental data into the animal respiratory tract heterogeneous particle deposition model for data correction to obtain a corrected numerical model of heterogeneous particle deposition distribution in the animal respiratory tract.

[0067] In a specific embodiment, the airflow equation is first selected according to the fluid flow state, and then the force analysis of the particle movement process is performed to determine the final force of the particles (such as gravity, drag force, Brownian force, etc.) to construct the particle momentum equation and the particle collision and agglomeration equation, and the expansion amplitude of the alveoli (dynamic grid parameters) is determined according to the respiratory tidal volume to construct the respiratory deformation equation. Finally, computational fluid dynamics software is used to select the airflow flow physics field, particle collision and agglomeration physics field, particle momentum equation, and geometric deformation physics field according to the determined and constructed equations, and combined with the guinea pig three-dimensional full respiratory tract geometric model to obtain the guinea pig rhythmic breathing three-dimensional high-humidity full respiratory tract particle deposition numerical model. The accuracy of the numerical model is corrected using the existing animal inhalation experimental results.

[0068] In a specific embodiment, the construction process mainly includes using CT slices and paraffin-embedded slices to respectively construct a three-dimensional respiratory tract geometric model of a guinea pig's real non-acinar area and a three-dimensional respiratory tract dense-paving geometric model of an idealized alveolar area, and then constructing a three-dimensional full respiratory tract geometric model of a guinea pig based on the above-mentioned segmented model. Finally, based on the three-dimensional full respiratory tract geometric model and mathematical theoretical analysis, a three-dimensional high-humidity full respiratory tract particle deposition numerical model of a guinea pig's rhythmic breathing is constructed.

[0069] 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 numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and the method for constructing the same.

[0070] Figure 2 A schematic diagram of a numerical model construction system for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal provided by an embodiment of the present invention specifically includes: Acquisition unit: acquires the image data of the animal's respiratory tract, particle properties, fluid flow type, and respiratory tidal volume; Reconstruction unit: performing three-dimensional reconstruction of the respiratory area through the image data to obtain a respiratory model; Motion unit: Analyze the force on particles through fluid flow type and particle properties to build airflow model and particle motion tracking model; Deformation unit: calculating the respiratory deformation of the respiratory model by the respiratory tidal volume to construct the respiratory deformation model; Deposition unit: The respiratory tract geometry model, particle motion tracking model, and respiratory tract deformation model are combined to obtain a numerical model of heterogeneous particle deposition distribution in the animal respiratory tract.

[0071] Figure 3 A schematic diagram of a numerical model construction device for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal provided by an embodiment of the present invention specifically includes: 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-mentioned numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and the construction method thereof.

[0072] The disclosed embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to execute any one of the above-mentioned numerical models for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and the method for constructing the same.

[0073] The embodiment of the present invention provides a particle deposition prediction method based on a particle deposition distribution numerical model, which specifically includes: Obtain breathing parameters, deformation parameters, particle collision parameters, and particle attribute parameters; A respiratory tract heterogeneous particle deposition model or a whole respiratory tract heterogeneous particle deposition model is obtained according to the above numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and the construction method thereof; Inputting the breathing parameters, deformation parameters, particle collision parameters, and particle attribute parameters into a respiratory tract heterogeneous particle deposition model or a full respiratory tract heterogeneous particle deposition model to obtain heterogeneous particle deposition prediction results; the prediction results include heterogeneous particle deposition rate and heterogeneous particle deposition distribution; In one embodiment, the heterogeneous particle deposition rate includes a regional deposition rate and a total deposition rate.

[0074] In one embodiment, the region includes one or more of the following: pharynx, trachea, left bronchus, right bronchus, left bronchiole, right bronchiole, left acinus, right acinus.

[0075] In one embodiment, the method further comprises calculating a particle deposition law, and calculating the deposition law of the heterogeneous particles according to the regional deposition rate and the total deposition rate.

[0076] In one embodiment, the method also includes analyzing the particle deposition mechanism, recording the movement trajectory of the particles during the deposition process, marking the deposition mechanism of each particle by comparing the particle deposition mechanism standard, and obtaining the deposition mechanism of heterogeneous particles in different regions based on the particle movement trajectory and the particle deposition mechanism.

[0077] In a specific embodiment, the prediction process mainly includes inputting and integrating respiratory parameters (frequency, tidal volume) and particle properties (particle size, density, hydrophilicity) into a particle deposition numerical model, predicting the particle deposition law through the model, and tracking the particle trajectory to obtain the particle deposition mechanism.

[0078] In a specific embodiment, the particle deposition prediction process is: I. Particle deposition numerical model integration: Set the input respiratory parameters, particle properties and other parameters in the particle deposition numerical model. Calculate the airflow velocity at the outlet and the expansion amplitude of the alveoli according to the respiratory frequency and tidal volume. Set the boundary condition of the outlet to the airflow velocity in the airflow flow physics field, and set the boundary condition of the inlet to the pressure of 0Pa. Set the deformation parameters of the alveoli in the geometric deformation physics field. Set the particle property parameters and the number of released particles in the particle deposition physics field, and set the particle collision parameters.

[0079] II. Particle deposition law prediction: Computational fluid dynamics software was used to run the particle deposition numerical model to predict the particle deposition law. After the calculation was completed, the particle deposition amount in different areas and the total amount of deposited particles in the complete respiratory tract were post-processed using computational fluid dynamics software. The particle deposition law was then calculated and the total deposition rate was calculated. DF total and regional sedimentation rate DF i Indicates that i Represents nasopharynx, trachea, left bronchus, right bronchus, left bronchiole, right bronchiole, left acinus, and right acinus.

[0080]

[0081]

[0082] III Particle motion trajectory tracking and deposition mechanism revealed: After the calculation is completed, the motion trajectory of the particles during the deposition process is tracked, such as Figure 8 As shown, by comparing the particle deposition mechanism standards, the deposition mechanism of each particle is marked, and then the deposition mechanism of particles with heterogeneous particle sizes in different regions is analyzed.

[0083] The disclosed embodiments of the present invention further provide a computer program product or system, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned particle deposition prediction method based on the particle deposition distribution numerical model.

[0084] An embodiment of the present invention provides a particle deposition prediction system based on a particle deposition distribution numerical model, which specifically includes: Parameter unit: obtain breathing parameters, deformation parameters, particle collision parameters, and particle attribute parameters; Model unit: obtaining a numerical model for heterogeneous particle deposition distribution in the respiratory tract or a numerical model for heterogeneous particle deposition distribution in the entire respiratory tract according to the above numerical model for predicting heterogeneous particle deposition distribution in the respiratory tract of an animal and its construction method; Prediction unit: input the breathing parameters, deformation parameters, particle collision parameters, and particle attribute 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 whole respiratory tract to obtain a deposition prediction result of heterogeneous particles; the prediction result includes a heterogeneous particle deposition rate and a heterogeneous particle deposition distribution; An embodiment of the present invention provides a particle deposition prediction device based on a particle deposition distribution numerical model, which specifically includes: 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 particle deposition distribution numerical model is executed.

[0085] The disclosed embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is any one of the above-mentioned particle deposition prediction methods based on the particle deposition distribution numerical model.

[0086] An embodiment of the present invention provides a method for constructing a geometric model of an animal's entire respiratory tract, comprising: Acquire imaging data and animal tissue section data; Performing three-dimensional reconstruction of the non-acinar region of the animal respiratory tract based on the image data to obtain a non-acinar region model; Performing three-dimensional reconstruction of the animal respiratory tract acinar region based on the animal tissue slice data to obtain an acinar region model; The non-acinar region model and the acinar region model are spliced ​​together to obtain a geometric model of the entire respiratory tract of the animal.

[0087] In one embodiment, the alveolar region is densely paves the alveoli according to the airway size and alveolar size in the tissue slice data to obtain a two-dimensional densely pave model, and three-dimensional stretching is performed based on the two-dimensional densely pave model to obtain a three-dimensional alveolar region model.

[0088] In another embodiment, the alveolar region generates three-dimensional Thiessen polygons through random point clouds, and then uses an optimization algorithm to construct an alveolar path within the three-dimensional Thiessen polygons, and uses four-sided face reconstruction and subdivision modeling to perform smoothing to obtain an idealized polyhedral alveolar model of the respiratory tract terminal; wherein, before smoothing, the polygonal faces passing through the alveolar path are deleted and the edges of the two adjacent three-dimensional Thiessen polygons are connected to obtain a flow channel connecting the alveoli, perform geometric repair, and create an idealized alveolar model entrance.

[0089] The disclosed embodiments of the present invention further 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 a geometric model of the entire respiratory tract of an animal.

[0090] An embodiment of the present invention provides a system for constructing a geometric model of the entire respiratory tract of an animal, specifically comprising: Data acquisition unit: acquires image data and animal tissue slice data; Non-acinar unit: performing three-dimensional reconstruction of the non-acinar region of the animal respiratory tract based on the image data to obtain a non-acinar region model; Alveolar unit: performing three-dimensional reconstruction of the animal respiratory tract alveolar region based on the animal tissue slice data to obtain an alveolar region model; Geometric unit: splicing the non-acinar region model and the acinar region model to obtain a geometric model of the entire respiratory tract of the animal; An embodiment of the present invention provides a device for constructing a geometric model of the entire respiratory tract of an animal, specifically comprising: A memory and a processor; the memory is used to store program instructions; the processor is used to call program instructions, and the program instructions are executed by any one of the above-mentioned methods for constructing a geometric model of the entire respiratory tract of an animal.

[0091] The disclosed embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is any one of the above-mentioned methods for constructing a geometric model of the entire respiratory tract of an animal.

[0092] The verification results of this verification embodiment show that the performance of this method can be improved relative to the default setting in order to adapt to the allocation of inherent weights. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiment described above is only schematic, for example, the division of the unit is only a logical function division, and there may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units. A person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above-mentioned embodiments may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0093] A person of ordinary skill in the art can understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned medium storage can be a read-only memory, a disk or an optical disk, etc.

[0094] The above is a detailed introduction to a computer device provided by the present invention. For a person skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and a method for constructing the same, characterized in that: include: Obtain imaging data of the animal's respiratory tract, particle properties, fluid flow pattern type, and respiratory tidal volume; Performing three-dimensional reconstruction of the respiratory tract region using the image data to obtain a respiratory tract model; The airflow model and particle motion tracking model are constructed by analyzing the particle force through the fluid flow type and particle properties; Calculating the respiratory tract deformation amount of the respiratory tract model by the respiratory tidal volume to construct a respiratory tract deformation model; The respiratory tract model, the particle motion tracking model and the respiratory tract deformation model are combined to obtain a numerical model of heterogeneous particle deposition distribution in the animal respiratory tract.

2. The numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal and the method for constructing the same according to claim 1, characterized in that: The force parameters are determined by 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, gravity, Brownian force, pressure gradient, virtual mass force, lift, and Bassett force.

3. The numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal and the method for constructing the same according to claim 1, characterized in that: The construction of the particle motion tracking model also includes particle collision analysis, through which a particle collision agglomeration equation is generated, and the particle motion tracking model is constructed through the particle collision agglomeration equation and the particle momentum equation.

4. The numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal and the method for constructing the same according to claim 1, characterized in that: The airway deformation is calculated by calculating the expansion ratio of the airway through the breathing tidal volume, and then calculated using the expansion ratio and the directional displacement of each plane during breathing; the plane normal velocity formula of the airway deformation is expressed as: in, i , j , k Respectively represent x , y , z The number of each plane perpendicular to the direction, l ( i or j or k )represent l ( i )or l ( j )or l ( k ) is the product of the displacement in the direction relative to the center of the alveolar top surface and the expansion ratio.

5. The numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and the method for constructing the same according to claim 1, characterized in that: The animal respiratory tract heterogeneous particle deposition model also includes model correction, which obtains animal inhalation experimental data, and inputs the animal inhalation experimental data into the animal respiratory tract heterogeneous particle deposition model for data correction to obtain a corrected animal respiratory tract heterogeneous particle deposition model.

6. The numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of an animal and the method for constructing the same according to claim 1, characterized in that: The three-dimensional reconstruction of the respiratory tract region also includes reconstruction of the acinus region to obtain tissue section data; Performing three-dimensional reconstruction of the acinar region based on the tissue section data to obtain an acinar region model; The image data is used to perform three-dimensional reconstruction of the non-acinar region of the respiratory tract to obtain a non-acinar region model; The acinar region model and the non-acinar region model are spliced ​​together to obtain a full respiratory tract geometric model; the respiratory tract deformation amount of the full respiratory tract model is calculated by respiratory tidal volume to construct a full 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 a numerical model of heterogeneous particle deposition distribution in the whole respiratory tract of animals.

7. A particle deposition prediction method based on a particle deposition distribution numerical model, characterized in that: include: Obtain breathing parameters, deformation parameters, particle collision parameters, and particle attribute parameters; According to the numerical model for predicting the deposition distribution of heterogeneous particles in the animal respiratory tract and the construction method thereof described in claims 1-6, a numerical model for the deposition distribution of heterogeneous particles in the respiratory tract or a numerical model for the deposition distribution of heterogeneous particles in the entire respiratory tract is obtained; The breathing parameters, deformation parameters, particle collision parameters, and particle attribute parameters are input 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 prediction result of the deposition distribution of heterogeneous particles; the prediction result includes a heterogeneous particle deposition rate and a heterogeneous particle deposition distribution.

8. A computer program product having a computer program or instructions thereon, characterized in that: include: The computer program or instructions are executed by the processor to implement the numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and the construction method thereof as described in claims 1-6, or to implement the particle deposition prediction method based on the particle deposition distribution numerical model as described in claim 7.

9. A computer device comprising a memory, a processor and a computer program or instruction stored in the memory, characterized in that: The computer program or instructions are executed by the processor to implement the numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and the construction method thereof as described in claims 1-6, or to implement the particle deposition prediction method based on the particle deposition distribution numerical model as described in claim 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: The computer program or instructions are executed by the processor to implement the numerical model for predicting the deposition distribution of heterogeneous particles in the respiratory tract of animals and the method for constructing the same as described in any one of claims 1 to 6, or to implement the particle deposition prediction method based on the numerical model of particle deposition distribution described in claim 7.

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