Aortic valve and ascending aorta in vitro evaluation methods, systems, devices, and media

Abstract

The present application belongs to the technical field of aortic valve and ascending aorta pathological evaluation, and particularly relates to an aortic valve and ascending aorta in-vitro evaluation method, system, device and medium, which is applied to a test system based on a patient personalized customized aortic valve and ascending aorta physical model, and the method comprises the following steps: in a simulation test process of a patient's aortic valve and ascending aorta physical model, PIV velocity field, multi-point pressure time sequence and flow time sequence are collected through a flowmeter, a pressure sensor and a high-frequency camera arranged in the aortic valve and ascending aorta physical model in advance, and corresponding time-domain-frequency-domain mechanical indexes are calculated according to the PIV velocity field, the multi-point pressure time sequence and the flow time sequence; a corresponding cycle consistency index is calculated according to a test cycle of the simulation test; all the time-domain-frequency-domain mechanical indexes and the cycle consistency index are integrated, and the condition of the patient's aortic valve and ascending aorta is evaluated according to the indexes, so as to serve as a basis for preoperative decision and instrument evaluation.

Description

Technical Field

[0001] This invention belongs to the field of pathological assessment technology of aortic valve and ascending aorta, specifically relating to an in vitro assessment method, system, device, and medium for aortic valve and ascending aorta. Background Technology

[0002] The aorta is a large blood vessel that originates from the left ventricle of the heart; the ascending section is called the ascending aorta. The aortic valve is a one-way valve located between the outlet of the left ventricle and the beginning of the ascending aorta. It typically consists of three semilunar leaflets (valve membranes) attached to the aortic annulus. Its function is to ensure that blood flows smoothly from the left ventricle into the aorta during cardiac contraction and to close tightly during cardiac relaxation, preventing blood from flowing back into the heart.

[0003] The main aortic valve diseases include aortic stenosis and aortic regurgitation, while diseases of the ascending aorta itself include ascending aortic aneurysm and ascending aortic dissection. Although they are not direct valvular diseases, they often involve the aortic annulus and root, leading to valvular dysfunction.

[0004] However, current preoperative and postoperative assessments of aortic valve and ascending aortic lesions heavily rely on the surgeon's experience and lack scientific, quantitative, and multi-parameter-based evaluation methods. Therefore, it is necessary to establish an in vitro assessment method for aortic valve and ascending aorta, taking into account the individual differences in the physiological and anatomical structures of different patients. This method would reveal the impact of individualized structural characteristics on parameters such as blood flow velocity, flow field distribution, and vessel wall stress, enabling rapid, quantitative, and standardized preoperative surgical planning, postoperative outcome prediction, disease pathology analysis, and the development of implantable interventional devices. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an in vitro assessment method for the aortic valve and ascending aorta. This method uses a personalized, customized physical model of the patient's aortic valve and ascending aorta for in vitro assessment. The assessment includes not only morphological parameters such as diameter and area, and simple mechanical parameters such as flow velocity and pressure, but also complex mechanical parameters such as wall shear force and relative particle residence time related to the vascular morphology of the aortic valve and ascending aorta. This assessment method is more scientific and reduces calculation errors caused by mathematical assumptions compared to parameters obtained through traditional virtual simulation calculations, resulting in more accurate parameter values.

[0006] This invention provides an in vitro assessment method for the aortic valve and ascending aorta, characterized in that it is applied to a testing system based on a patient-customized aortic valve and ascending aorta solid model. The method includes: controlling the fluid pressure based on a preset pressure-time curve to allow fluid to flow from the left atrial buffer chamber into the left atrial model and then into the left ventricular model via a one-way valve; driving the squeezing chamber to periodically move based on a preset frequency to eject fluid from the left ventricular model, which then flows through a flow meter into the aortic valve and ascending aorta solid model and into the ascending aorta fluid buffer chamber; wherein the fluid flows back from the ascending aorta fluid buffer chamber to the left atrial buffer chamber to form a closed loop; during the simulation test of the patient's aortic valve and ascending aorta solid model, the flow meter, pressure sensor, and high-frequency camera pre-arranged in the aortic valve and ascending aorta solid model acquire the PIV velocity field, multi-point pressure time series, and flow time series, and calculate the corresponding time-domain-frequency domain mechanical parameters based on them, including: a. calculating the instantaneous wall shear force based on the acquired PIV velocity field, and the instantaneous wall shear force... ;in, The dynamic viscosity of the liquid. a. The wall tangential velocity gradient is obtained by interpolation / fitting the PIV velocity field of the near-wall profile; b. The time average value is calculated based on the instantaneous wall shear force, and the time average value... ;in, c. Calculate the oscillating shear index based on the instantaneous wall shear force, and the oscillating shear index is a preset test cycle; d. Calculate the flow disturbance intensity based on the acquired PIV velocity field, including: calculating the pulsatility characteristic parameters of blood flow based on multiple continuously acquired velocity parameters and their corresponding average velocities; converting the pulsatility characteristic parameters into velocity magnitude representations and performing normalization processing to obtain the flow disturbance intensity; wherein, the flow disturbance intensity Represented as ,and Let be the blood flow velocity at the k-th time point. The average velocity of blood flow within a test cycle; the corresponding cycle consistency index is calculated based on the test cycle of the simulated test, including: calculating the relative deviation between the velocity field of each test cycle and the average velocity across cycles, and performing normalization processing to obtain the total jitter index; wherein, the velocity field of a cycle is formed by all velocity parameters of that cycle; the cycle consistency index is obtained by averaging the total jitter index; wherein, the cycle consistency index Represented as ,and It represents the velocity field of a test cycle; it integrates all time-domain and frequency-domain mechanical indicators and cycle consistency indicators, and uses them to assess the condition of the patient's aortic valve and ascending aorta, serving as the basis for preoperative decision-making and device evaluation.

[0007] In one embodiment of the present invention, before testing the solid models of the patient's aortic valve and ascending aorta, the method further includes: acquiring CT images of the patient's heart and preprocessing them to obtain images of the aortic valve and ascending aorta; inputting the images of the aortic valve and ascending aorta into a first convolutional neural network to segment the valve, main vascular trunk, and vascular branches in the aortic valve / ascending aorta; mapping the segmentation results of the first convolutional neural network back to the original space through inverse transformation and performing morphological repair to obtain vascular images of the aortic valve and ascending aorta; generating models of the aortic valve and ascending aorta through inverse 3D modeling based on the images of the patient's aortic valve and ascending aorta, and the vascular images of the aortic valve and ascending aorta; and customizing personalized solid models of the aortic valve and ascending aorta through 3D printing based on the patient's aortic valve and ascending aorta models.

[0008] In one embodiment of the present invention, the preprocessing step of the CT image includes: performing grayscale thresholding on the CT image to extract an image region containing only the aortic valve region and the ascending aorta region; optimizing the image region containing only the aortic valve region and the ascending aorta region through morphological processing, and cropping sub-blocks containing only the aortic valve region and the ascending aorta region from the optimized image region; standardizing and unifying the size of several sub-blocks, and integrating them to obtain the image of the aortic valve region and the ascending aorta region.

[0009] In one embodiment of the present invention, the step of inputting the images of the aortic valve region and the ascending aorta region into a first convolutional neural network to segment the valve, main vascular trunk, and vascular branches in the aortic valve / ascending aorta includes: based on the segmentation result of the first convolutional neural network, identifying whether there is an aneurysm or dissection in the patient's ascending aorta; if so, then using the ascending aorta mask output by the first convolutional neural network, straightening the curved ascending aorta in the image using a skeletonization and centerline optimization algorithm; inputting the processed image into a second convolutional neural network to distinguish between the true lumen, aneurysm, or false lumen in the ascending aorta; wherein, the segmentation results of the first convolutional neural network and the second convolutional neural network are mapped back to the original space through inverse transformation to obtain the vascular image of the ascending aorta region.

[0010] In one embodiment of the present invention, the steps of generating models of the aortic valve and ascending aorta include: preprocessing images of the aortic valve and ascending aorta of the patient, as well as vascular images of the aortic valve and ascending aorta; wherein the preprocessing operations include registration, denoising, and pixel-level intensity normalization; converting the processed image data into voxels or surface meshes, and generating an initial model of the aortic valve / ascending aorta based on it; and performing smoothing, hole repair, topology correction, and thin-wall / thick-wall consistency verification on the initial model to identify and repair broken edges and unreasonable geometry, thereby obtaining the final model of the aortic valve and ascending aorta.

[0011] In one embodiment of the present invention, after customizing a personalized solid model of the aortic valve and ascending aorta by 3D printing based on the model of the patient's aortic valve and ascending aorta, the method further includes: acquiring CT images of the solid models of the aortic valve and ascending aorta, and comparing them with images of the aortic valve and ascending aorta obtained by processing CT images of the patient's heart, in order to verify whether the solid models of the aortic valve and ascending aorta meet the standards.

[0012] In one embodiment of the present invention, image comparison is performed according to the following formula:

[0013] ,

[0014] in, This indicates the comparison result. CT images representing a solid model of the aortic valve / ascending aorta. This indicates an image of the aortic valve / ascending aorta obtained through CT image processing of the patient's heart.

[0015] This invention also provides an in vitro assessment system for the aortic valve and ascending aorta, applied to a testing system based on a patient-customized physical model of the aortic valve and ascending aorta. The system includes: a simulation testing module for controlling fluid pressure based on a preset pressure-time curve, causing fluid to flow from the left atrial buffer chamber into the left atrial model and then through a one-way valve into the left ventricular model; and driving the squeezing chamber to periodically move based on a preset frequency, causing the left ventricular model to eject fluid, which then flows through a flow meter into the physical models of the aortic valve and ascending aorta; wherein the fluid flows back from the physical model of the ascending aorta to the left atrial buffer chamber to form a closed loop; and an index calculation module for acquiring PIV velocity fields, multi-point pressure time sequences, and flow time sequences from flow meters, pressure sensors, and high-frequency cameras pre-positioned in the physical models of the aortic valve and ascending aorta during the simulation testing of the patient's aortic valve and ascending aorta, and calculating corresponding time-domain-frequency domain mechanical indices, including: a. calculating instantaneous wall shear force based on the acquired PIV velocity field, and the instantaneous wall shear force... ;in, The dynamic viscosity of the liquid. a. The wall tangential velocity gradient is obtained by interpolation / fitting the PIV velocity field of the near-wall profile; b. The time average value is calculated based on the instantaneous wall shear force, and the time average value... ;in, c. Calculate the oscillating shear index based on the instantaneous wall shear force, and the oscillating shear index is a preset test cycle; d. Calculate the flow disturbance intensity based on the acquired PIV velocity field, including: calculating the pulsatility characteristic parameters of blood flow based on multiple continuously acquired velocity parameters and their corresponding average velocities; converting the pulsatility characteristic parameters into velocity magnitude representations and performing normalization processing to obtain the flow disturbance intensity; wherein, the flow disturbance intensity Represented as ,and Let be the blood flow velocity at the k-th time point. The average velocity of blood flow within a test cycle; and, based on the test cycle of the simulated test, calculating the corresponding cycle consistency index, including: calculating the relative deviation between the velocity field of each test cycle and the average velocity across cycles, and performing normalization processing to obtain the total jitter index; wherein, the velocity field of a cycle is formed by all velocity parameters of that cycle; averaging the total jitter index to obtain the cycle consistency index; wherein, the cycle consistency index Represented as ,and The velocity field represents a test cycle; the in vitro assessment module integrates all time-domain and frequency-domain mechanical parameters and cycle consistency parameters, and assesses the condition of the patient's aortic valve and ascending aorta based on them, serving as the basis for preoperative decision-making and device evaluation.

[0016] An electronic device includes a processor coupled to a memory storing program instructions that, when executed by the processor, implement the method described above.

[0017] A computer-readable storage medium includes a program that, when run on a computer, causes the computer to perform the method described above.

[0018] The beneficial effects of this invention are as follows: Based on the individualized physiological and anatomical characteristics of different patients, this invention can perform in vitro analysis and evaluation of the aortic valve and ascending aortic vessels, and quickly output diverse functional indicators such as blood flow velocity, blood ejection angle, blood flow pressure, fluid wall shear force, and relative particle residence time. Furthermore, based on the individualized morphology of the aortic valve and ascending aortic vessels of each patient, in vitro testing can be conducted through a customized physical model to output device performance evaluation results that are more in line with the actual situation of the patient. This can assist in preoperative surgical planning, postoperative outcome prediction, device optimization design, and personalized medical customization. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 This is a schematic flowchart of an in vitro assessment method for the aortic valve and ascending aorta provided in one embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the structure of the aortic valve and ascending aorta solid model testing device provided in one embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of the aortic valve and ascending aorta solid model testing device provided in another embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram illustrating the fabrication of a personalized aortic valve and ascending aorta solid model provided in one embodiment of the present invention.

[0024] Figure 5This is a flowchart illustrating an in vitro assessment method for the aortic valve and ascending aorta provided in another embodiment of the present invention.

[0025] Figure 6 This is a schematic diagram of the structure of an in vitro assessment system for the aortic valve and ascending aorta provided in one embodiment of the present invention;

[0026] Figure 7 This is a structural block diagram of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0027] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0029] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0030] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0031] Example 1

[0032] Please see Figure 1 As shown, an in vitro assessment method for the aortic valve and ascending aorta is applied to a testing system based on a patient-customized physical model of the aortic valve and ascending aorta, comprising:

[0033] Step S100: Control the liquid pressure based on the preset pressure timing curve so that the liquid flows from the left atrial buffer chamber into the left atrial model and then into the left ventricular model through the one-way valve.

[0034] In step S200, the squeezing chamber is driven to periodically move based on a preset frequency so that the left ventricular model ejects liquid, which flows into the aortic valve and the solid model of the ascending aorta via a flow meter, and then flows into the ascending aortic fluid buffer chamber; wherein, the liquid flows back from the ascending aortic fluid buffer chamber to the left atrial buffer chamber to form a closed loop.

[0035] Step S300: During the simulation test of the patient's aortic valve and ascending aorta solid model, flow meters, pressure sensors, and high-frequency cameras pre-positioned in the aortic valve and ascending aorta solid model are used to collect PIV velocity field, multi-point pressure time series, and flow time series, and the corresponding time-domain-frequency domain mechanical parameters are calculated based on these parameters, including:

[0036] a. Calculate the instantaneous wall shear force based on the collected PIV velocity field, and the instantaneous wall shear force ;in, The dynamic viscosity of the liquid. The tangential velocity gradient of the wall is obtained by interpolation / fitting the PIV velocity field of the near-wall profile.

[0037] b. Calculate the time average value based on the instantaneous wall shear force, and the time average value... ;in, The preset testing period (heartbeat period).

[0038] c. Calculate the oscillatory shear index based on the instantaneous wall shear force, and the oscillatory shear index .

[0039] d. Calculating the flow disturbance intensity based on the acquired PIV velocity field, including: calculating the pulsatility characteristic parameters of blood flow based on multiple continuously acquired velocity parameters and their corresponding average velocities; converting the pulsatility characteristic parameters into velocity magnitude representations and performing normalization processing to obtain the flow disturbance intensity; wherein, the flow disturbance intensity Represented as ,and The blood flow velocity being collected, This represents the average velocity corresponding to blood flow.

[0040] Step S400, calculating the corresponding cycle consistency index based on the test cycle of the simulated test, includes: calculating the relative deviation between the velocity field of each test cycle and the average velocity across cycles, and performing normalization processing to obtain the total jitter index; wherein, the velocity field of a cycle is formed by all velocity parameters of that cycle; averaging the total jitter index to obtain the cycle consistency index; wherein, the cycle consistency index... Represented as ,and This represents the velocity field over one test cycle.

[0041] Step S500 integrates all time-domain and frequency-domain mechanical parameters and periodic consistency parameters, and assesses the condition of the patient's aortic valve and ascending aorta based on them, so as to serve as the basis for preoperative decision-making and device evaluation.

[0042] Specifically, in practical applications, the testing equipment for the aortic valve and ascending aorta solid model can be found in [reference needed]. Figure 2 , 3 As shown, by conducting functional and mechanical tests on personalized aortic valve and ascending aorta solid models in vitro, the physiological and anatomical characteristics of patients can be accurately simulated, facilitating in vitro analysis and evaluation of the aortic valve and ascending aorta vessels. Simultaneously, simulation tests on the aortic valve and ascending aorta solid models can output device performance evaluation results that better reflect the patient's actual condition, thereby assisting in preoperative surgical planning, postoperative outcome prediction, device optimization design, and personalized medical customization.

[0043] The testing device comprises a fluid supply and buffer chamber (left atrial buffer chamber), a one-way valve, a left ventricular model and a variable pressure squeezing chamber (or a servo-driven reciprocating chamber), optional compliance or impedance units, a flow meter (electromagnetic / ultrasound optional), an ascending aortic fluid buffer chamber, and connecting tubing and interfaces. The aortic valve and ascending aortic models are installed in the testing device in a replaceable manner. Accordingly, the aortic valve and ascending aortic models can be customized based on the patient's CT or MRI images, thus enabling in vitro simulation testing.

[0044] During the test, fluid needs to be driven from the left atrial buffer chamber into the left atrial model, and then through a one-way valve into the left ventricular model. At the same time, the pump periodically drives the squeezing chamber to produce blood ejection from the left ventricular model. The ejected fluid flows through a flow meter into the aortic valve and ascending aorta solid model, and then into the ascending aorta fluid buffer chamber, and finally flows back to the left atrial buffer chamber, forming a closed loop.

[0045] Therefore, pressure sensors, flow meters, and high-frequency cameras can be installed at the inlet / outlet and several key locations to collect relevant mechanical parameters. Simultaneously, programmable drivers (servo / stepping / reciprocating pumps or extrusion mechanisms) can be used to drive the extrusion chamber with adjustable amplitude and frequency to simulate heart rate and ejection curves. Pressure / flow data from pressure sensors / flow meters can also be accepted for closed-loop control to accurately reproduce the target pressure-flow waveform.

[0046] Furthermore, to improve the physiological relevance of the in vitro pulsating flow reproduction, three-way valve interfaces can be reserved and connected at several key measurement locations (such as near the valve orifice, the middle segment of the ascending aorta, or the aortic sinus) of the aortic valve and ascending aorta solid model, so as to connect the measured part to the high-speed pressure sensor without disrupting the closed loop.

[0047] In practical applications, operators can directly import clinically measured pressure-time curves or manually edit and generate pressure waveforms, i.e., pressure-time curves, through the human-computer interaction interface. During operation, the pump controller will perform closed-loop regulation at a preset sampling frequency and send the pump drive commands (displacement / speed / valve position) to the pump drive unit in real time to drive the liquid to complete a closed loop, simulating the physiological condition of the patient's aortic valve and ascending aorta.

[0048] Finally, based on the mechanical parameters collected during the testing process, the corresponding time-domain and frequency-domain mechanical indices and period consistency indices are calculated to generate a standardized report (including the original time series, phase mean field, near-wall profile, and wall stress risk heat map) as the basis for preoperative decision-making and instrument evaluation.

[0049] It should be added here that the flow disturbance intensity in the time-frequency domain mechanical index is a dimensionless "chaos index" obtained by comparing the "velocity jitter amplitude" (root mean square) with the "average velocity". The larger this index is, the more unstable the flow is. It can be used to quantify the instability of disturbances in the high shear region and recirculation region under the quantization lobe, and as a threshold quantity for risk warning or test condition screening.

[0050] Therefore, by and The mean is removed by subtraction, retaining only the pulsation characteristics of blood flow. The square root of the square is then used to prevent positive and negative pulsation characteristics from canceling each other out, and this indicator is expressed as velocity. Finally, normalization is applied for cross-sectional comparison of flow disturbance intensity under different test conditions. It can be written as Alternatively, it can be written as the root mean square of the three-component composite velocity, or as a monotonic transformation of the second moment such as the standard deviation, etc., without any restrictions.

[0051] Furthermore, the cycle consistency index treats the "entire waveform / entire velocity sample" of each test cycle as a velocity field. With cross-cycle To determine the magnitude of the difference between the two values, the ||·|| operation is used to quantify the difference into a scalar value that considers strength only, regardless of sign. This is then divided by ||·|| to achieve dimensionlessness, avoiding the dimensional / scaling effect where a large mean naturally implies a large difference. Finally, the average across all test periods is used to transform the total occasional fluctuations into an overall consistency level. This level represents the degree of inconsistency of the test across different periods, with smaller values ​​indicating greater consistency.

[0052] Therefore, without changing the essential meaning of "comparing the relative deviation of each cycle from the mean across cycles," all fall within the scope of protection of this application. For example, the final averaging method could be an arithmetic mean of the test cycles, a weighted average (based on heart rate stability, cardiac phase weights, etc.), or other methods; or complementary parameters of the cycle consistency index could be used, such as... Alternatively, other parameters can be used for calculation, such as pressure and flow rate.

[0053] At the same time, periodic consistency indicators can also be used. Replace the norm in the expression, such as compressing "square-integral-square root" into the L2 norm:

[0054] .

[0055] Modifications and refinements made by those skilled in the art to the embodiments of the present invention without departing from the spirit of the present invention still fall within the scope of the invention application patent.

[0056] In one specific embodiment, a physical model of the patient's aortic valve and ascending aorta can be personalized using the following method:

[0057] Taking a CT image of a patient's heart as an example, the complete CT image is first segmented using a grayscale threshold, and then the voxel region of interest containing only the aortic valve and ascending aorta is extracted, which is the corresponding image region. Morphological processing is then used to eliminate noise and fill in gaps after threshold segmentation, making the extracted image region more complete.

[0058] Secondly, based on this, sub-blocks containing only the aortic valve and ascending aorta are cropped to reduce redundant information and lower data dimensionality, thereby improving the computational efficiency of subsequent convolutional neural networks. Simultaneously, several sub-blocks are standardized and their sizes are uniformized before being integrated.

[0059] Furthermore, the processed image data is input into a convolutional neural network (first convolutional neural network) to achieve multi-class segmentation, namely, segmenting the valves, main trunk, and branches of the aortic valve / ascending aorta. If an aneurysm or dissection exists in the ascending aorta, the curved ascending aorta in the image is straightened using skeletonization and centerline optimization algorithms based on the ascending aorta mask output by the first convolutional neural network. This simplifies the relative positions of the true lumen, aneurysm, or false lumen in the ascending aorta. The processed image is then input into a second convolutional neural network to accurately distinguish the true lumen, aneurysm, or false lumen in the ascending aorta.

[0060] Finally, the segmentation results of the first and second convolutional neural networks are mapped back to the original space through inverse transformation and morphological restoration is performed to obtain vascular images of the aortic valve and ascending aorta.

[0061] Based on the above, corresponding 3D models of the aortic valve and ascending aorta can be generated through reverse 3D modeling using images of the patient's aortic valve and ascending aorta, as well as vascular images of these locations. For example, DICOM / tomography images are acquired and preprocessed (registration, denoising, and pixel-level intensity normalization) to preprocess the images of the patient's aortic valve and ascending aorta, and the vascular images of these locations. The processed image data is then converted into voxels or surface meshes to generate an "intuitive 3D model" (initial model of the aortic valve / ascending aorta) based on the original images. The initial model is then smoothed, hole repaired, topology corrected, and thin-wall / thick-wall consistency checked to identify and repair fracture edges and unreasonable geometry, resulting in the final models of the aortic valve and ascending aorta. Finally, customized solid models of the aortic valve and ascending aorta can be created using 3D printing, supporting stereolithography / powder bed fusion 3D printing, silicone / resin molding, CNC machining, and arbitrary combinations thereof. Meanwhile, transparent or semi-transparent shell materials can be selected according to experimental requirements to ensure optical imaging conditions.

[0062] It is understandable that, such as Figure 4 As shown, by customizing a physical model of the patient's aortic valve and ascending aorta, it can be ensured that the morphology of the inner surface of the cavity (valve opening area, calcification morphology, sinus structure, bending radius, etc.) is mapped one-to-one with the patient's image. If necessary, replaceable valve leaflets or calcification components can be embedded in the cavity for repeated testing.

[0063] Furthermore, by segmenting the patient's CT images and reverse-engineering them to generate a manufacturable STL / CAD model, a physical model can be customized via 3D printing / casting. Correspondingly, after printing, the geometry of the physical model can be measured using optical measurements or a second CT scan to calibrate the error values ​​between the model and the actual physiological structure.

[0064] To address this, CT images of the aortic valve and ascending aorta solid models can be obtained and compared with images of the aortic valve and ascending aorta obtained by processing CT images of the patient's heart to verify whether the solid models of the aortic valve and ascending aorta meet the standards.

[0065] Specifically, the scale index for defining error values, namely geometric reproducibility (comparison results), is as follows: =Volume overlap / Reference volume, which can be further expressed as This allows for image comparison.

[0066] in, CT images representing a solid model of the aortic valve / ascending aorta. This indicates an image of the aortic valve / ascending aorta obtained through CT image processing of the patient's heart.

[0067] Understandably, the geometric reproducibility provided above is used to calibrate the overlap between medical images and printed physical models as a constraint on "morphological reliability" in the "morphological-mechanical" closed loop. However, it is not limited to this one comparison method. For example, the difference between the two can also be measured by Hausdorff distance and its monotonic transformation. Furthermore, the two can also be represented in other data formats such as CT / segmented voxels, meshes, or point clouds. There are no restrictions on this.

[0068] Based on the above, the actual procedure of the in vitro assessment method for the aortic valve and ascending aorta provided in this embodiment can be found in [reference needed]. Figure 5 The illustration includes:

[0069] Step 1: Image Acquisition and Intelligent Segmentation

[0070] Acquire patient CT / MRI images (at least one frame from systole and diastole to display the morphology of the largest valve leaflet opening), and segment the aortic root, aortic sinus, valve leaflets, and ascending aorta volume / surface contours through image processing. Output reference geometry data (3D surface or voxel data) to ensure that the fabricated solid model is consistent with the actual geometry of the patient.

[0071] Step 2: Rapid Manufacturing and Calibration of Individualized Physical Models

[0072] Based on the segmented data obtained from the above steps, a manufacturable STL / CAD model is generated through reverse modeling, and a solid model is then created through 3D printing / casting. The entrance area and shape of the chamber are determined by the shape of the largest valve leaflet opening. Furthermore, after printing, the geometry of the solid model is obtained through optical measurement or a secondary CT scan to calibrate the error value between the model and the actual physiological structure.

[0073] Step 3: Module Assembly and Sensor Placement

[0074] The physical model was loaded into the chamber of the test device, and pressure sensors were installed at key locations (subvalvular, near the valve orifice, mid-segment of the ascending aorta, and sinus region). Flow meters were placed at the inlet / outlet, and an optical window was reserved for the PIV and a high-frequency camera was arranged.

[0075] Step 4: Initial Test (Filling of Two Water Channels and Evacuation of Gas)

[0076] Drive water circuit (referred to as circuit A): Pump + drive medium → squeeze the "squeeze chamber" to drive the deformation of the "left ventricular model" (Note: Circuit A is not connected to the fluid circuit under test).

[0077] The test water path (denoted as path B) is as follows: left atrial buffer → left atrial model → left ventricular model → flow through personalized model → ascending aortic buffer → return to left atrium (forming a closed loop), and the collected mechanical parameters are all from path B (velocity field, pressure, WSS, etc.).

[0078] Step 5: Set the target and start the pump (using a pressure target or a flow rate target).

[0079] The user interface imports preset pressure time-series curves and uses pressure feedback to better reproduce the clinical pressure field.

[0080] Step 6: Synchronous Data Acquisition: PIV + Multi-point Pressure + Flow

[0081] According to the set phase-triggered scheme (phase-locked or time-resolved), PIV camera, pressure sampling and flow meter recording are synchronously triggered. The acquired mechanical parameters include, but are not limited to, v(x,t) (velocity field), p(x_i,t) (multi-point pressure time series) and Q(t) (flow time series).

[0082] Step 7: Calculate the quantized time-domain and frequency-domain mechanical properties and periodicity consistency properties.

[0083] Including but not limited to instantaneous wall shear force, time average (TAWSS), oscillating shear index (OSI), and flow disturbance intensity.

[0084] Step 8: Summarize the index data obtained above, and give the confidence interval based on repeated cycle or repeated assembly test.

[0085] Step 9: Generate a standardized report

[0086] Further analysis of the patient's aortic valve and ascending aorta physiological condition was conducted based on the actual clinical situation.

[0087] In another specific embodiment, considering the patient's aortic valve and ascending aorta are implanted with prosthetic valves, the prosthetic valve can be loaded and released into the chamber of the ascending aorta model via a specialized intervention after the physical model is fabricated, and the release morphology of the prosthetic valve will be consistent with the actual situation. Finally, the time-domain and frequency-domain mechanical properties and periodic consistency properties of the implanted and non-implanted prosthetic valves are compared, the differences are analyzed and statistically tested to generate a standardized report.

[0088] It should be noted that the steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0089] Example 2

[0090] Please see Figure 6 As shown, this embodiment also provides an in vitro assessment system for the aortic valve and ascending aorta, including:

[0091] The simulation test module 10 is used to control the liquid pressure based on a preset pressure timing curve so that the liquid flows from the left atrial buffer chamber into the left atrial model and then into the left ventricular model through a one-way valve; and to drive the squeezing chamber to periodically move based on a preset frequency so that the left ventricular model ejects the liquid and flows into the solid model of the aortic valve and the solid model of the ascending aorta through a flow meter; wherein the liquid flows back from the solid model of the ascending aorta to the left atrial buffer chamber to form a closed loop.

[0092] The index calculation module 20 is used to collect PIV velocity field, multi-point pressure time series, and flow time series data through flow meters, pressure sensors, and high-frequency cameras pre-positioned in the aortic valve and ascending aorta solid models during simulation testing of the patient's aortic valve and ascending aorta solid models, and to calculate the corresponding time-domain and frequency-domain mechanical indices based on these data, including:

[0093] a. Calculate the instantaneous wall shear force based on the collected PIV velocity field, and the instantaneous wall shear force ;in, The dynamic viscosity of the liquid. The wall tangential velocity gradient is obtained by interpolation / fitting of the PIV velocity field in the near-wall profile.

[0094] b. Calculate the time average value based on the instantaneous wall shear force, and the time average value... ;in, This is the preset test cycle;

[0095] c. Calculate the oscillatory shear index based on the instantaneous wall shear force, and the oscillatory shear index ;

[0096] d. Calculate the flow disturbance intensity based on the collected PIV velocity field, and the flow disturbance intensity Among them, the phase-averaged velocity field , For PIV velocity field, for;

[0097] Furthermore, a corresponding periodic consistency index is calculated based on the test cycle of the simulated test, and the periodic consistency index... ;in, This represents the number of test cycles. for.

[0098] The in vitro assessment module 30 is used to integrate all time-domain and frequency-domain mechanical indicators and periodic consistency indicators, and to assess the condition of the patient's aortic valve and ascending aorta based on them, so as to serve as the basis for preoperative decision-making and device evaluation.

[0099] It should be noted that the aortic valve and ascending aorta in vitro assessment system provided in the above embodiments and the aortic valve and ascending aorta in vitro assessment method provided in Embodiment 1 above belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the aortic valve and ascending aorta in vitro assessment method provided in Embodiment 1 above can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0100] Example 3

[0101] Please see Figure 7 As shown, embodiments of this application also provide an electronic device, including a memory 2, a processor 1, and a program stored in the memory and executable on the processor, wherein the processor executes the steps of any of the methods described above.

[0102] The memory includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory can include both internal and external storage units of the electronic device. The memory can be used not only to store application software and various types of data installed on the electronic device, but also to temporarily store data that has been output or will be output.

[0103] In some embodiments, a processor may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions. This includes combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor is the control unit of the electronic device, connecting various components of the device via various interfaces and lines. It executes programs or modules stored in the memory and calls data stored in the memory to perform various functions and process data within the electronic device.

[0104] The processor executes the operating system of the electronic device and various installed applications. The processor executes the applications to implement the steps in the above method embodiments.

[0105] For example, the program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of program instruction segments capable of performing a specific function, which describe the execution process of the program in the electronic device.

[0106] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some of the functions of the various embodiments of the present invention.

[0107] In summary, this invention can perform in vitro analysis and evaluation of the aortic valve and ascending aortic vessels based on the individualized physiological and anatomical characteristics of different patients, and quickly output diverse functional indicators such as blood flow velocity, blood ejection angle, blood flow pressure, fluid wall shear force, and relative particle residence time. Furthermore, based on the individualized morphology of the aortic valve and ascending aortic vessels, it can conduct in vitro testing through a customized physical model, outputting device performance evaluation results that are more in line with the actual situation of the patient, thereby assisting in preoperative surgical planning, postoperative outcome prediction, device optimization design, and personalized medical customization.

[0108] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An in vitro assessment method for the aortic valve and ascending aorta, characterized in that, A testing system applied to a patient-specific aortic valve and ascending aorta solid model, the method comprising: The liquid pressure is controlled based on a preset pressure-time curve so that the liquid flows from the left atrial buffer chamber into the left atrial model and then into the left ventricular model through a one-way valve. The compression chamber is driven to periodically move based on a preset frequency, so that the left ventricular model ejects fluid, which flows through a flow meter into the aortic valve and the physical model of the ascending aorta, and then into the fluid buffer chamber of the ascending aorta; wherein, the fluid flows back from the fluid buffer chamber of the ascending aorta to the buffer chamber of the left atrium to form a closed loop; During the simulation testing of the patient's aortic valve and ascending aorta solid models, flow meters, pressure sensors, and high-frequency cameras pre-positioned in the aortic valve and ascending aorta solid models were used to acquire PIV velocity fields, multi-point pressure time series, and flow time series. Based on these data, the corresponding time-domain and frequency-domain mechanical parameters were calculated, including: a. Calculate the instantaneous wall shear force based on the collected PIV velocity field, and the instantaneous wall shear force ;in, The dynamic viscosity of the liquid. The wall tangential velocity gradient is obtained by interpolation / fitting of the PIV velocity field in the near-wall profile. b. Calculate the time average value based on the instantaneous wall shear force, and the time average value... ;in, This is the preset test cycle; c. Calculate the oscillatory shear index based on the instantaneous wall shear force, and the oscillatory shear index ; d. Calculating the flow disturbance intensity based on the acquired PIV velocity field, including: calculating the pulsatility characteristic parameters of blood flow based on multiple continuously acquired velocity parameters and their corresponding average velocities; converting the pulsatility characteristic parameters into velocity magnitude representations and performing normalization processing to obtain the flow disturbance intensity; wherein, the flow disturbance intensity Represented as ,and Let be the blood flow velocity at the k-th time point. The average velocity of blood flow within a test cycle; The calculation of the corresponding cycle consistency index based on the test cycle of the simulated test includes: calculating the relative deviation between the velocity field of each test cycle and the average velocity across cycles, and performing normalization processing to obtain the total jitter index; wherein, the velocity field of a cycle is formed by all velocity parameters of that cycle; averaging the total jitter index to obtain the cycle consistency index; wherein, the cycle consistency index... Represented as ,and This represents the velocity field over one test cycle; Integrate all time-domain and frequency-domain mechanical parameters and periodic consistency parameters, and use them to assess the condition of the patient's aortic valve and ascending aorta, so as to serve as the basis for preoperative decision-making and device evaluation.

2. The in vitro assessment method for the aortic valve and ascending aorta according to claim 1, characterized in that, Prior to testing the patient's aortic valve and ascending aorta solid model, the following steps were also taken: CT images of the patient's heart were acquired and preprocessed to obtain images of the aortic valve and ascending aorta. Images of the aortic valve and ascending aorta are input into the first convolutional neural network to segment the valves, main vessels, and branches in the aortic valve / ascending aorta. The segmentation results of the first convolutional neural network are mapped back to the original space by inverse transformation and morphological restoration is performed to obtain vascular images of the aortic valve and ascending aorta. Based on images of the patient's aortic valve and ascending aorta, as well as vascular images of the aortic valve and ascending aorta, models of the aortic valve and ascending aorta are generated through reverse 3D modeling. Based on the patient's aortic valve and ascending aorta model, a personalized solid model of the aortic valve and ascending aorta is customized through 3D printing.

3. The in vitro assessment method for the aortic valve and ascending aorta according to claim 2, characterized in that, The steps for preprocessing the CT images include: The CT images are segmented by grayscale thresholding to extract image regions containing only the aortic valve and ascending aorta. The image region containing only the aortic valve and ascending aorta is optimized by morphological processing, and a sub-block containing only the aortic valve and ascending aorta is cropped from the optimized image region. Several sub-blocks were standardized and their dimensions were uniformized, and then integrated to obtain images of the aortic valve and ascending aorta.

4. The in vitro assessment method for the aortic valve and ascending aorta according to claim 2, characterized in that, The steps of inputting the images of the aortic valve region and the ascending aorta region into a first convolutional neural network to segment the valves, main vessels, and branches in the aortic valve / ascending aorta include: Based on the segmentation results of the first convolutional neural network, the presence of aneurysm or dissection in the patient's ascending aorta is identified: If so, then based on the ascending aorta mask output by the first convolutional neural network, the curved ascending aorta in the image is straightened using skeletonization and centerline optimization algorithms. The processed images are then input into a second convolutional neural network to distinguish between true lumens, aneurysms, or false lumens in the ascending aorta. Specifically, the segmentation results of the first and second convolutional neural networks are mapped back to the original space through inverse transformation to obtain vascular images of the ascending aorta.

5. The in vitro assessment method for the aortic valve and ascending aorta according to claim 2, characterized in that, The steps to generate models of the aortic valve and ascending aorta include: The images of the aortic valve and ascending aorta of the patient, as well as the vascular images of the aortic valve and ascending aorta, were preprocessed. The preprocessing operations included registration, denoising, and pixel-level intensity normalization. The processed image data is converted into voxels or surface meshes, and an initial model of the aortic valve / ascending aorta is generated based on it. The initial model is smoothed, hole repaired, topology corrected, and thin-wall / thick-wall consistency checked to identify and repair fracture edges and unreasonable geometry, resulting in the final model of the aortic valve and ascending aorta.

6. The in vitro assessment method for the aortic valve and ascending aorta according to claim 2, characterized in that, Following the process of creating customized physical models of the aortic valve and ascending aorta based on patient-generated models using 3D printing, the process also includes: CT images of the aortic valve and ascending aorta solid models were acquired and compared with images of the aortic valve and ascending aorta obtained by processing CT images of the patient's heart to verify whether the solid models of the aortic valve and ascending aorta met the standards.

7. The in vitro assessment method for the aortic valve and ascending aorta according to claim 6, characterized in that, Image comparison is performed using the following formula: ， in, This indicates the comparison result. CT images representing a solid model of the aortic valve / ascending aorta. This indicates an image of the aortic valve / ascending aorta obtained through CT image processing of the patient's heart.

8. An in vitro assessment system for the aortic valve and ascending aorta, characterized in that, A testing system applied to a patient-specific aortic valve and ascending aorta solid model, the system comprising: The simulation test module is used to control the liquid pressure based on a preset pressure-time curve, so that the liquid flows from the left atrial buffer chamber into the left atrial model and then into the left ventricular model through a one-way valve; and to drive the squeezing chamber to periodically move based on a preset frequency, so that the left ventricular model ejects liquid and flows into the solid model of the aortic valve and the solid model of the ascending aorta through a flow meter; wherein the liquid flows back from the solid model of the ascending aorta to the left atrial buffer chamber to form a closed loop; The index calculation module is used to collect PIV velocity field, multi-point pressure time series, and flow time series data through flow meters, pressure sensors, and high-frequency cameras pre-positioned in the aortic valve and ascending aorta solid models during simulation testing of patients. Based on these data, the module calculates the corresponding time-domain and frequency-domain mechanical indices, including: a. Calculate the instantaneous wall shear force based on the collected PIV velocity field, and the instantaneous wall shear force ;in, The dynamic viscosity of the liquid. The wall tangential velocity gradient is obtained by interpolation / fitting of the PIV velocity field in the near-wall profile. b. Calculate the time average value based on the instantaneous wall shear force, and the time average value... ;in, This is the preset test cycle; c. Calculate the oscillatory shear index based on the instantaneous wall shear force, and the oscillatory shear index ; d. Calculating the flow disturbance intensity based on the acquired PIV velocity field, including: calculating the pulsatility characteristic parameters of blood flow based on multiple continuously acquired velocity parameters and their corresponding average velocities; converting the pulsatility characteristic parameters into velocity magnitude representations and performing normalization processing to obtain the flow disturbance intensity; wherein, the flow disturbance intensity Represented as ,and Let be the blood flow velocity at the k-th time point. The average velocity of blood flow within a test cycle; And, based on the test cycle of the simulated test, calculate the corresponding cycle consistency index, including: calculating the relative deviation between the velocity field of each test cycle and the average velocity across cycles, and performing normalization processing to obtain the total jitter index; wherein, the velocity field of a cycle is formed by all velocity parameters of that cycle; averaging the total jitter index to obtain the cycle consistency index; wherein, the cycle consistency index... Represented as ,and This represents the velocity field over one test cycle; The in vitro assessment module integrates all time-domain and frequency-domain mechanical parameters and periodic consistency parameters, and assesses the condition of the patient's aortic valve and ascending aorta based on these parameters, serving as the basis for preoperative decision-making and device evaluation.

9. An electronic device, characterized in that, The method includes a processor coupled to a memory storing program instructions, which, when executed by the processor, implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Includes a program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 7.

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