A quantitative analysis method for dynamic diffusion of ICG fluorescence in the lungs

Abstract

The present invention relates to the technical field of pulmonary vascular function analysis methods, and specifically to a pulmonary ICG fluorescence dynamic diffusion quantitative analysis method, comprising the following steps: acquiring a patient's lung slice image; receiving fluorescent tracer injection information; constructing a three-dimensional lung model based on the lung slice image; establishing a blood vessel model and a blood flow model based on the three-dimensional lung model; simulating the fluorescent tracer diffusion process in the three-dimensional lung model and the blood vessel model according to the fluorescent tracer injection information; calculating the flow rate of all pipelines using an optical flow method; determining key time points according to preset conditions; calculating the flow value of each pipeline at the key time point of the tracing process based on the key time points; calculating the maximum flow change rate and the maximum flow difference according to the flow values; acquiring physiological parameter information corresponding to the three-dimensional lung model, and generating a vascular function evaluation model, thereby improving the accuracy of blood vessel recognition, especially significantly improving the recognition ability of tiny blood vessels.

Description

Technical Field

[0001] The present invention relates to the technical field of pulmonary vascular function analysis methods, and in particular to a pulmonary ICG fluorescence dynamic diffusion quantitative analysis method. Background Art

[0002] With the continuous advancement of medical imaging technology, diagnostic and assessment methods for lung diseases are also progressing. Traditional methods for assessing pulmonary vascular function primarily rely on technologies such as enhanced CT scans and magnetic resonance angiography. While these methods can provide certain anatomical information, they still have significant shortcomings in dynamic blood flow assessment and analysis of microvascular function.

[0003] In recent years, indocyanine green (ICG) fluorescence imaging has garnered widespread attention due to its low invasiveness and high sensitivity. This technique involves injecting an ICG tracer and using near-infrared fluorescence imaging to capture the dynamic distribution of ICG within blood vessels, thereby enabling the assessment of vascular function. However, existing ICG fluorescence imaging techniques still face numerous challenges when applied to assessing pulmonary vascular function.

[0004] First, the complex three-dimensional structure of the lungs makes it difficult for traditional two-dimensional fluorescence imaging to accurately reflect the spatial distribution and functional status of blood vessels. Second, existing methods often struggle to precisely quantify the dynamic diffusion of ICG within blood vessels, which limits the accurate assessment of hemodynamic parameters. Furthermore, due to the lack of effective data processing and analysis methods, existing technologies struggle to extract valuable vascular function information from massive amounts of dynamic image data.

[0005] Furthermore, existing ICG fluorescence imaging technology has significant limitations in identifying and assessing the function of tiny blood vessels in the lungs. Dysfunction in these tiny vessels is often an early sign of various lung diseases, such as pulmonary embolism and pulmonary hypertension. However, due to limitations in resolution and sensitivity, existing technologies struggle to effectively assess the function of these tiny vessels.

[0006] Finally, existing methods for assessing pulmonary vascular function generally have problems such as complex operation, long analysis time, and high radiation dose, which not only increases the burden on patients but also limits the widespread application of these methods in clinical practice.

[0007] In view of the above problems, there is an urgent need for a new method that can achieve accurate quantitative analysis of pulmonary vascular function, especially effective evaluation of microvascular function. Summary of the Invention

[0008] To address the above technical issues, the present invention proposes a method for optimizing the efficiency of multi-band RF energy collection. This method should provide high-resolution, three-dimensional vascular function information, accurately capture the dynamic diffusion of ICG, and extract valuable vascular function parameters from complex image data. Furthermore, this method should be simple to operate, rapidly analyze, and provide low radiation dose to meet the needs of clinical practice.

[0009] The present invention provides a method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs, comprising the following steps:

[0010] Acquire lung slice images of the patient;

[0011] receiving fluorescent tracer injection information;

[0012] constructing a three-dimensional lung model based on the lung slice image;

[0013] Establishing a blood vessel model and a blood flow model based on the three-dimensional lung model;

[0014] simulating a fluorescent tracer diffusion process in the three-dimensional lung model and the blood vessel model according to the fluorescent tracer injection information;

[0015] Based on the diffusion process, the optical flow method is used to calculate the flow rate of all pipes;

[0016] Determine key time points based on preset conditions;

[0017] Based on the key time points, calculating the flow value of each pipeline at the key time points of the tracing process;

[0018] Calculating the maximum flow rate change rate and the maximum flow rate difference according to the flow value;

[0019] Acquiring physiological parameter information corresponding to the three-dimensional lung model;

[0020] A vascular function evaluation model is generated based on the maximum flow rate change rate, the maximum flow difference, the physiological parameter information, and the light intensity curve.

[0021] Preferably, the constructing of the three-dimensional lung model specifically includes:

[0022] Dividing the three-dimensional lung model into N×N grids, where N is a preset number of grids;

[0023] Define each mesh as a node;

[0024] Adjacent nodes are defined as pipelines to form a virtual vascular network;

[0025] The pipes in the three-dimensional lung model are consistent with the pipes in the blood vessel model.

[0026] Preferably, the calculating the flow rates of all pipelines specifically includes:

[0027] Based on the optical flow method, the flow velocities of all pipelines are calculated;

[0028] Calculate the maximum flow rate Q for each pipe based on the pipe radius and the flow velocity, where:

[0029] Q=πr 2 v

[0030] Where π is the circumference of a circle, r is the inner radius of the pipe, and v is the maximum flow velocity;

[0031] Calculate the maximum flow of each node, where v i,j is the flow rate into pipe j, v i+1,j is the flow rate out of pipe j at the ith point.

[0032] Preferably, the method for determining the key time point includes:

[0033] Obtain the difference between two adjacent tracer concentrations;

[0034] Set the standard deviation value;

[0035] When the difference between any two adjacent tracer concentrations is greater than the standard deviation, the corresponding two time points are set as key time points.

[0036] Preferably, the method for calculating the maximum flow rate change rate and the maximum flow difference includes:

[0037] Calculate the maximum flow rate change:

[0038]

[0039] Calculate the maximum flow difference:

[0040] Q n -Q n-1

[0041] Where Δt n is the time interval, Q n is the flow value at the nth key time point.

[0042] Preferably, the generating of the vascular function evaluation model further comprises:

[0043] Set the maximum flow standard value of the node;

[0044] Analyze the relationship between the maximum flow rate change rate, the maximum flow difference, and the node maximum flow value and the standard value;

[0045] Based on the analysis results, the vascular function evaluation model is established.

[0046] Preferably, the method further comprises the step of generating the light intensity curve:

[0047] Based on the key time points, obtaining the tracer concentration of the fluorescent tracer at the vascular nodes inside the three-dimensional lung model;

[0048] Plot the tracer concentration curve over time;

[0049] The light intensity curve is generated according to the tracer concentration curve.

[0050] Preferably, the fluctuation amplitude of the light intensity curve is proportional to the light penetration.

[0051] As an advantage, the method further includes the following steps:

[0052] Drawing a tracer concentration curve that changes with time based on the tracer concentration of the vascular node at several key time points;

[0053] generating a light intensity curve according to the tracer concentration curve;

[0054] performing anomaly detection on a plurality of the light intensity curves;

[0055] Extract abnormal features;

[0056] Build a dataset for identifying unusual patterns.

[0057] As an option, it also includes:

[0058] Analyzing the input lung ICG fluorescence image based on the vascular function evaluation model;

[0059] Generate pulmonary vascular function assessment report;

[0060] The pulmonary vascular function assessment report includes vascular perfusion status, abnormal area identification and oxygen supply capacity assessment.

[0061] By combining advanced image processing techniques, fluid dynamics analysis, and machine learning algorithms, this invention proposes a novel method for quantitative analysis of the dynamic diffusion of ICG fluorescence in the lungs. This method not only accurately constructs a three-dimensional pulmonary vascular model but also accurately captures the dynamic diffusion of ICG within the vessels through optical flow analysis. By introducing the concepts of virtual vascular networks and key time points, this method enables simplified representation and efficient analysis of complex vascular structures. Furthermore, by comprehensively considering multiple hemodynamic parameters and physiological indicators, this method establishes a comprehensive model for assessing vascular function.

[0062] The core innovation of the present method lies in its unique multidisciplinary approach. By organically combining computer vision techniques (such as optical flow), fluid dynamics analysis, and machine learning algorithms, this method achieves precise quantification and in-depth analysis of the dynamic diffusion process of ICG fluorescence. This interdisciplinary approach not only overcomes the limitations of traditional techniques in microvascular assessment but also significantly improves the accuracy and comprehensiveness of vascular function assessment.

[0063] Furthermore, while achieving high-precision analysis, the method of the present invention significantly reduces the radiation dose received by the patient and shortens the examination time. This balance between effectiveness and efficiency gives the method broad prospects for clinical application.

[0064] Specifically, the main beneficial effects brought about by the method of the present invention include:

[0065] 1. The accuracy of blood vessel recognition has been significantly improved, especially for small blood vessels (as small as 0.5mm in diameter). This makes it possible to detect small blood vessel lesions at an early stage.

[0066] 2. By accurately capturing the dynamic diffusion process of ICG, this method achieves high-precision measurement of hemodynamic parameters, with a relative error as low as ±5.2%. This provides a reliable basis for accurately assessing vascular function.

[0067] 3. This method significantly improves the detection rate of abnormal areas (up to 92.5%), which is of great significance for the early diagnosis and precise positioning of lung diseases.

[0068] 4. While ensuring high-quality diagnostic information, this method reduces the radiation dose received by patients by nearly half and shortens examination time by 40%. This not only reduces the burden on patients but also improves the efficiency of medical institutions.

[0069] 5. By establishing a comprehensive vascular function assessment model, this method provides a reliable basis for the formulation of individualized treatment plans, and is expected to significantly improve the accuracy and effectiveness of lung disease treatment.

[0070] In summary, the proposed method for quantitative analysis of dynamic ICG fluorescence diffusion in the lungs innovatively addresses several key challenges faced by existing technologies, enabling high-precision, low-invasive assessment of pulmonary vascular function. This method not only offers significant technical advantages but also holds broad clinical application prospects, potentially bringing significant breakthroughs in the early diagnosis, precision treatment, and prognostic assessment of lung diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 Flow chart of the method of the present invention.

[0072] Figure 2Flowchart of the diffusion simulation and flow velocity calculation of the present invention.

[0073] Figure 3 Flow chart of the flow analysis of the present invention.

[0074] Figure 4 Flowchart established for the evaluation model of the present invention. DETAILED DESCRIPTION

[0075] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following detailed description, along with the accompanying drawings and preferred embodiments, includes a detailed description of the specific implementations, structures, features, and effects thereof. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0076] Unless defined otherwise, 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 invention belongs.

[0077] See Figure 1-4 The present invention proposes a quantitative analysis method for dynamic diffusion of ICG fluorescence in the lungs, comprising an acquisition step, a processing step and an output step. In the acquisition step, a lung slice image of the patient is first acquired. These images are usually obtained by CT or MRI scanning, with high resolution and clear tissue structure information. Preferably, in one embodiment of the present invention, the layer thickness of the CT scan can be set to 1-2 mm to ensure that sufficiently detailed lung structure information is obtained. At the same time, the method also needs to receive fluorescent tracer injection information, including important parameters such as injection time and dose. Typically, the injection dose of ICG can be 0.1-0.5 mg / kg body weight, and the injection time is usually 5 to 10 seconds before the start of the scan.

[0078] In the processing step, the present invention constructs a three-dimensional lung model based on the acquired lung slice images. This step involves complex image processing algorithms, such as image segmentation and three-dimensional reconstruction. Preferably, in one embodiment of the present invention, a deep learning algorithm, such as U-Net or Mask R-CNN, can be used to improve the accuracy and efficiency of model construction. For example, when using U-Net for lung tissue segmentation, an input image size of 256x256 and a network structure with a depth of 5 layers can be used to strike a balance between accuracy and computational efficiency.

[0079] Next, this method further establishes a vascular model and a blood flow model based on the constructed three-dimensional lung model. The vascular model needs to accurately describe the anatomical structure of the pulmonary blood vessels, while the blood flow model needs to take into account the fluid mechanics of the blood. When establishing the blood flow model, the viscosity (usually between 3.5-5.5 mPa·s) and density (about 1060 kg / m 3 ) and other parameters.

[0080] Subsequently, the present invention simulates the fluorescent tracer diffusion process within the 3D lung and vascular models based on the received fluorescent tracer injection information. This process is typically simulated using computational fluid dynamics (CFD) methods, which can accurately reflect the movement and diffusion of the tracer within the blood vessels. In CFD simulations, an appropriate turbulence model (such as the k-ε model) can be used to describe blood flow characteristics.

[0081] During the simulation, this method uses optical flow to calculate flow velocities in all channels. Optical flow is a commonly used motion estimation algorithm. In this invention, it is applied to the dynamic diffusion analysis of fluorescent tracers. This innovative application enables this method to accurately capture the motion characteristics of the tracer in the blood vessels. Preferably, the Horn-Schunck algorithm can be used to implement the optical flow method, where the smoothness parameter α can be set between 0.1 and 1.0, and the number of iterations is typically 50 to 200.

[0082] Based on pre-set conditions, this method identifies key time points. These key time points are crucial for accurately assessing vascular function. Preferably, key time points can be determined based on the rate of change of the tracer concentration. For example, when the rate of change of concentration exceeds 10% / s, this moment can be defined as a key time point.

[0083] Based on the determined key time points, this method calculates the flow value of each pipeline at the key time point of the tracing process. The flow value is calculated using the following formula:

[0084] Q=πr 2 v

[0085] Where Q is the flow rate, r is the pipe radius, and v is the flow velocity. Based on the calculated flow value, this method further calculates the maximum flow rate change and the maximum flow difference. These two parameters are important for evaluating vascular function. The formula for calculating the maximum flow rate change is:

[0086]

[0087] Among them, Q i and Q i+1 are the flow values ​​of two adjacent key time points, t i and t i+1is the corresponding time point.

[0088] The calculation formula for the maximum flow difference is:

[0089] Maximum flow difference = max i (Q i+1 -Q i )

[0090] These calculation results provide important basis for subsequent vascular function assessment.

[0091] During the processing, the method also acquires physiological parameter information corresponding to the 3D lung model. These parameters may include the patient's age, gender, weight, smoking history, etc., which are important for accurately assessing vascular function.

[0092] In the output step, this method generates a vascular function assessment model based on the calculated maximum flow rate change, maximum flow difference, physiological parameter information, and light intensity curve. This model comprehensively considers multiple factors and can more comprehensively and accurately assess pulmonary vascular function. Preferably, a machine learning algorithm (such as a random forest or support vector machine) can be used to construct the assessment model, with a feature importance threshold of 0.05 to filter out the most influential features.

[0093] Finally, this method outputs a vascular function assessment model that can intuitively display the vascular function status of different lung regions, providing an important reference for clinical diagnosis and treatment.

[0094] Preferably, the specific step of constructing the 3D lung model in the present invention includes dividing the 3D lung model into N×N grids, where N is a preset number of grids. Preferably, the value of N can be determined based on the required accuracy and computing resources, and typically a power of 2 value such as 32, 64, or 128 can be selected. For example, when N=64, a good balance between accuracy and computing efficiency can be achieved.

[0095] Next, this method defines each mesh as a node and the connections between adjacent nodes as channels, thus forming a virtual vascular network. This meshing approach simplifies the complex pulmonary vascular structure, facilitating subsequent numerical calculations and analysis. Importantly, the channels in the 3D lung model remain consistent with those in the vascular model, ensuring a consistent correspondence between the virtual network and the actual vascular structure.

[0096] Preferably, the specific step of calculating the flow velocities of all pipes in the present invention first calculates the flow velocities of all pipes based on the optical flow method. The application of the optical flow method enables this method to accurately capture the motion characteristics of the fluorescent tracer in the blood vessels. Preferably, the Lucas-Kanade algorithm or the Horn-Schunck algorithm can be used to implement the optical flow method. For example, when using the Horn-Schunck algorithm, the smoothness parameter α can be set to 0.5 and the number of iterations to 100 to achieve a balance between accuracy and computational efficiency.

[0097] Then, based on the pipe radius and flow velocity, this method calculates the maximum flow Q of each pipe. The calculation formula is:

[0098] Q=πr 2 v

[0099] Where π is the circumference of a circle, r is the inner radius of the pipe, and v is the maximum flow velocity. This formula, based on the principles of fluid mechanics, accurately describes the relationship between the flow rate in a circular pipe and the pipe radius and flow velocity.

[0100] Finally, the method calculates the maximum flow at each node. In this step, considering the conservation of flow at the node, the inflow is equal to the outflow. Therefore, for node j, we have:

[0101]

[0102] Among them, v i,j is the flow rate into pipe j, v i+1,j is the flow rate out of the i-th channel j. This equation ensures the flow balance in the virtual vascular network.

[0103] Preferably, the method for determining the key time point in the present invention includes obtaining the difference between two adjacent tracer concentrations. This step is intended to capture significant changes in tracer concentration, thereby identifying time points that are important for vascular function assessment.

[0104] Next, the method sets a standard deviation value. This standard deviation serves as a threshold for determining whether a concentration change is significant. Preferably, this threshold can be set based on the actual application scenario and requirements. For example, the standard deviation value can be set to 5% of the average concentration. This value is chosen based on experience to capture meaningful concentration changes while avoiding misjudgments due to noise or minor fluctuations.

[0105] Finally, when the difference between any two adjacent tracer concentrations exceeds a set standard deviation, the method sets the corresponding two time points as critical time points. This method effectively identifies important moments in the tracer diffusion process, providing key information for subsequent vascular function assessment.

[0106] Preferably, the method for calculating the maximum flow rate change and the maximum flow difference in the present invention comprises the following steps:

[0107] First, calculate the maximum flow rate change rate. The calculation formula is:

[0108] (Q n -Q n-1 ) / Δt n

[0109] Among them, Q n is the flow value at the nth key time point, Q n-1 is the flow value at the n-1th key time point, Δt n is the time interval between these two time points. This formula reflects the degree of change in flow per unit time and is an important indicator for evaluating vascular function.

[0110] Then, calculate the maximum flow difference. The calculation formula is:

[0111] Q n -Q n-1

[0112] This difference directly reflects the absolute value of the flow change between two adjacent key time points and can capture the sudden change of flow.

[0113] By calculating these two metrics, this method can comprehensively assess the functional status of blood vessels at different time points, providing important input for subsequent vascular function assessment models. Preferably, the maximum flow rate change rate and flow difference can be selected as feature inputs into the assessment model to capture the most significant changes in vascular function.

[0114] Through the above steps, it can be seen that the quantitative analysis method of dynamic diffusion of pulmonary ICG fluorescence of the present invention has the following advantages: First, it combines advanced image processing technology and fluid dynamics analysis to accurately construct a pulmonary vascular model and simulate the diffusion process of the fluorescent tracer. Second, by introducing the optical flow method and the concept of key time points, this method can accurately quantify the dynamic diffusion process and provide rich information on vascular function. Finally, by comprehensively considering multiple parameters and establishing an evaluation model, this method can comprehensively and accurately assess pulmonary vascular function, providing strong support for clinical diagnosis and treatment.

[0115] As preferably, the step of generating a vascular function assessment model in the present invention further includes setting a standard value for the maximum flow rate of a node. The setting of this standard value is crucial for accurately assessing vascular function. Preferably, in one embodiment of the present invention, the maximum flow rate distribution of a node in a normal population under different factors such as age, gender, and weight can be obtained based on a large amount of clinical data, thereby determining a reasonable standard value range. For example, for adult males, the standard flow rate of the pulmonary lobe aorta can be set to 4-6 L / min, while for adult females, this value may be slightly lower, set to 3.5-5.5 L / min.

[0116] Next, the method analyzes the maximum flow rate of change, the maximum flow difference, and the relationship between the node maximum flow value and the standard value. This step aims to comprehensively assess the functional status of the blood vessels. Specifically, the maximum flow rate of change reflects the vascular response to changes in blood flow, the maximum flow difference indicates the magnitude of the change in vascular capacity, and the comparison of the node maximum flow value with the standard value directly reflects the normal function of the blood vessels.

[0117] Based on the above analysis results, the present invention establishes a vascular function assessment model. This model takes into account multiple factors and can evaluate pulmonary vascular function more comprehensively and accurately. Preferably, a machine learning algorithm can be used to construct the assessment model, such as a random forest or a support vector machine (SVM). For example, when using the random forest algorithm, the number of trees can be set to 100-500 and the maximum depth to 10-20 to strike a balance between model complexity and generalization ability. The input features of the model may include the maximum flow rate change, the maximum flow difference, the ratio of the node maximum flow value to the standard value, etc. The output can be a score of 0-100, indicating the health of the vascular function.

[0118] Preferably, the present invention also includes a step of generating a light intensity curve. First, based on the determined key time points, the method obtains the tracer concentration of the fluorescent tracer at the vascular nodes inside the three-dimensional lung model. This step requires precise image processing technology to extract accurate concentration information from the fluorescence image. Preferably, an image segmentation algorithm (such as threshold segmentation or region growing method) can be used to identify the vascular region, and then the fluorescence intensity is quantified by pixel intensity analysis to infer the tracer concentration.

[0119] Next, the method plots a time-varying tracer concentration curve. This curve visually demonstrates the diffusion of the tracer within the blood vessels. Preferably, a spline interpolation method can be used to smooth the curve to reduce the influence of measurement noise. For example, cubic spline interpolation can be used with a control point interval of 1 to 2 seconds to achieve a balance between smoothness and detail preservation.

[0120] Finally, the method generates a light intensity curve based on the tracer concentration curve. This step takes into account the relationship between fluorescence intensity and tracer concentration, which can generally be assumed to be linear within a certain range. Preferably, this relationship can be determined through preliminary calibration experiments. For example, a relationship similar to I = kC + b can be obtained, where I is the light intensity, C is the concentration, and k and b are coefficients to be determined.

[0121] Preferably, the fluctuation amplitude of the light intensity curve in the present invention is proportional to the light penetration. This characteristic reflects the absorption and scattering effect of biological tissue on fluorescence. Specifically, the stronger the light penetration, the deeper the fluorescent signal can penetrate into the tissue, thereby generating a larger fluctuation amplitude. Preferably, the light penetration coefficient of different tissue types can be determined experimentally. For example, for lung tissue, the penetration coefficient of near-infrared light (such as fluorescence emitted by ICG) may be between 0.5 and 1.0 mm. -1 This information can be used to correct the light intensity curve and improve the accuracy of vascular function assessment.

[0122] Preferably, the present invention also includes an abnormal pattern identification step. First, based on the tracer concentration at several key time points in the vascular node, the method plots a time-varying tracer concentration curve. This step is similar to the one described above, but the purpose here is to identify abnormal patterns.

[0123] The method then generates a light intensity curve based on the tracer concentration curve. This step is also similar to the one described above, but here we focus specifically on the anomalous features of the curve.

[0124] Next, the method performs anomaly detection on several light intensity curves. This step aims to identify vascular function patterns that are inconsistent with normal physiological conditions. Preferably, various anomaly detection algorithms can be used, such as Isolation Forest or a single-class SVM. For example, when using the Isolation Forest algorithm, the contamination parameter can be set to 0.1, meaning that approximately 10% of the samples are expected to be anomalous.

[0125] Based on anomaly detection, this method extracts anomaly features. These features may include peaks, valleys, rates of rise, rates of fall, and so on. Preferably, dimensionality reduction techniques such as principal component analysis (PCA) or autoencoders can be used to extract the most representative anomaly features. For example, when using PCA, the number of principal components required to explain 95% of the variance can be selected; this number is typically between 3 and 10.

[0126] Finally, this method establishes a dataset for identifying abnormal patterns. This dataset will serve as the foundation for subsequent abnormal pattern recognition. Preferably, semi-supervised learning methods can be used to enhance the representativeness of the dataset. For example, a small number of labeled abnormal samples and a large number of unlabeled samples can be used to expand the number of abnormal samples through pseudo-labeling techniques.

[0127] Preferably, the present invention also includes a step of analyzing the input lung ICG fluorescence image based on the vascular function assessment model. This step applies the previously established assessment model to new image data, enabling vascular function assessment of unknown samples. Preferably, a sliding window technique can be used to process large lung images. The window size can be set to 64x64 or 128x128 pixels, and the step size can be set to half the window size to ensure sufficient overlap and smooth assessment results.

[0128] Finally, this method generates a pulmonary vascular function assessment report. This report integrates all the previous analysis results and provides an important reference for clinical diagnosis. Preferably, the report can include the following sections: first, vascular perfusion status, which can be visualized using a heat map to visually display the blood flow status of different regions; second, abnormal area identification, which can highlight vascular areas with potential problems using different colors or markers; and finally, oxygen supply capacity assessment, which can calculate the oxygen supply capacity index of each lung lobe or smaller area based on blood flow and oxygen saturation information.

[0129] Through the above steps, it can be seen that the quantitative analysis method of dynamic diffusion of ICG fluorescence in the lungs of the present invention can not only comprehensively assess vascular function, but also identify abnormal patterns and generate intuitive assessment reports. This method combines advanced image processing technology, fluid dynamics analysis, and machine learning algorithms to provide a powerful tool for the early diagnosis and precise treatment of lung diseases. In particular, in the diagnosis of diseases such as pulmonary embolism and pulmonary hypertension, this method is expected to significantly improve the accuracy and timeliness of diagnosis, thereby improving patient prognosis.

[0130] In order to further verify the superiority of the quantitative analysis method of dynamic diffusion of ICG fluorescence in the lungs of the present invention, the present invention selected a group of typical cases for example testing and compared them with the traditional CT enhanced scanning method.

[0131] Example 1: The patient was a 55-year-old male, 175 cm tall, 70 kg in weight, with a 10-year smoking history, who recently developed chest tightness and shortness of breath. We first examined the patient using the method of the present invention. The scanning parameters were set as follows: CT layer thickness 1.5 mm, ICG injection dose 0.3 mg / kg body weight, and injection time 7 seconds before scanning. U-Net was used for lung tissue segmentation, the network input image size was 256x256, and the network depth was 5 layers. When constructing the virtual vascular network, we selected N = 64 for grid division. The optical flow method used the Horn-Schunck algorithm, set the smoothness parameter α = 0.5, and the number of iterations to 100.

[0132] Comparative Example 1: The same patient underwent a conventional enhanced CT scan the day after undergoing the examination using the method of the present invention. The scanning parameters were the same as those in Example 1, but conventional iodine contrast agent was used for the enhanced scan.

[0133] Testing indicators, methods and results:

[0134] The present invention selected the following key indicators for comparison to verify the superiority of the method of the present invention:

[0135] 1. Vessel recognition accuracy: Calculate the ratio of correctly identified vessels to the total number of vessels through manual labeling.

[0136] 2. Minimum detectable vessel diameter: The minimum vessel diameter that can be accurately identified and evaluated.

[0137] 3. Hemodynamic parameter measurement accuracy: The relative error of blood flow velocity measurement is calculated by comparing with the ultrasound Doppler measurement results.

[0138] 4. Abnormal area detection rate: By comparing with the clinical diagnosis results, calculate the ratio of the number of correctly detected abnormal areas to the total number of abnormal areas.

[0139] 5. Radiation dose: The radiation dose received by the patient during the examination.

[0140] 6. Inspection time: the total time from the start of the scan to obtaining the final assessment report.

[0141] The test results are shown in the following table:

[0142]

[0143]

[0144] The above results demonstrate that the method of the present invention significantly outperforms traditional enhanced CT scanning methods across all key indicators. Particularly noteworthy is its ability to identify and assess vessels as small as 0.5 mm in diameter, significantly enhancing the detection of microvascular lesions. Furthermore, the significant improvement in the accuracy of hemodynamic parameter measurements makes the assessment of pulmonary vascular function more accurate and reliable.

[0145] Furthermore, the method of the present invention is particularly advantageous in detecting abnormal areas. In this patient's examination, the method successfully detected a tiny pulmonary embolism lesion in the right upper lobe, which had not been clearly identified on conventional enhanced CT scans. This fully demonstrates the advantages of the method of the present invention in early diagnosis and precise localization.

[0146] Notably, this method significantly reduces the radiation dose received by patients while ensuring high-quality diagnostic information. This is particularly important for patients requiring long-term follow-up. This shortened examination time helps improve the efficiency of medical institutions and reduces the burden on patients.

[0147] Based on the above examples and a large number of clinical practices, the optimal implementation parameters of the method of the present invention are summarized as follows:

[0148] 1. CT scanning parameters: slice thickness 1.0-1.5 mm, tube voltage 120 kV, tube current automatic modulation.

[0149] 2. ICG injection: dose 0.3-0.4 mg / kg body weight, injection time 5-8 seconds before scanning.

[0150] 3. Image segmentation: Use U-Net or Mask R-CNN, with an input image size of 256x256 or 512x512, and a network depth of 5-7 layers.

[0151] 4. Virtual vascular network: N = 64 or 128, depending on the specific situation.

[0152] 5. Optical flow method: using Horn-Schunck algorithm, smoothness parameter α = 0.4-0.6, number of iterations 100-200.

[0153] 6. Anomaly detection: The isolation forest algorithm is used, and the contamination parameter is set to 0.05-0.15.

[0154] This set of parameters ensures high-precision analysis results while also taking into account computational efficiency and clinical practicality. It should be noted that for different patient groups (such as children, pregnant women, or the elderly), some parameters may need to be appropriately adjusted to ensure safety and diagnostic effectiveness.

[0155] In summary, the present method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs achieves high-precision, low-invasive assessment of pulmonary vascular function through the innovative combination of optical flow, virtual vascular network construction, and machine learning techniques. This method not only improves the accuracy and timeliness of lung disease diagnosis but also provides a reliable basis for the development of personalized treatment plans.

[0156] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs, characterized in that: The following steps are involved: Acquire lung slice images of the patient; receiving fluorescent tracer injection information; constructing a three-dimensional lung model based on the lung slice image; Establishing a blood vessel model and a blood flow model based on the three-dimensional lung model; simulating a fluorescent tracer diffusion process in the three-dimensional lung model and the blood vessel model according to the fluorescent tracer injection information; Based on the diffusion process, the optical flow method is used to calculate the flow rate of all pipes; Determine key time points based on preset conditions; Based on the key time points, calculating the flow value of each pipeline at the key time points of the tracing process; Calculating the maximum flow rate change rate and the maximum flow rate difference according to the flow value; Acquiring physiological parameter information corresponding to the three-dimensional lung model; generating a vascular function assessment model based on the maximum flow rate change rate, the maximum flow difference, the physiological parameter information, and the light intensity curve; The constructing of the three-dimensional lung model specifically includes: Dividing the three-dimensional lung model into N×N grids, where N is a preset number of grids; Define each mesh as a node; Adjacent nodes are defined as pipelines to form a virtual vascular network; The pipes in the three-dimensional lung model are consistent with the pipes in the blood vessel model; The method for determining the key time point comprises: Obtain the difference between two adjacent tracer concentrations; Set the standard deviation value; When the difference between any two adjacent tracer concentrations is greater than the standard deviation, the corresponding two time points are set as key time points; The generating of the vascular function evaluation model further comprises: Set the maximum flow standard value of the node; Analyze the relationship between the maximum flow rate change rate, the maximum flow difference, and the node maximum flow value and the standard value to obtain an analysis result; Based on the analysis results, establishing the vascular function assessment model; The step of generating the light intensity curve is further included: Based on the key time points, obtaining the tracer concentration of the fluorescent tracer at the vascular nodes inside the three-dimensional lung model; Plot the tracer concentration curve over time; The light intensity curve is generated according to the tracer concentration curve.

2. The method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs according to claim 1, wherein: The calculation of the flow rates of all pipelines specifically includes: Based on the optical flow method, the flow velocities of all pipelines are calculated; Calculate the maximum flow rate Q for each pipe based on the pipe radius and the flow velocity, where: ； Where π is the circumference of a circle, r is the inner radius of the pipe, and v is the maximum flow velocity; Calculate the maximum flow of each node, where is the flow rate into pipe j at the ith point, is the flow rate out of pipe j at the ith point.

3. The method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs according to claim 1, wherein: The method for calculating the maximum flow rate change and the maximum flow difference includes: Calculate the maximum flow rate change: ； Calculate the maximum flow difference: ； in, is the time interval, is the flow value at the nth key time point.

4. The method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs according to claim 1, characterized in that: The fluctuation amplitude of the light intensity curve is proportional to the light penetration.

5. The method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs according to claim 1, characterized in that: It also includes the abnormal pattern recognition step: Drawing a tracer concentration curve that changes with time based on the tracer concentrations of the vascular nodes at the key time points; generating a light intensity curve according to the tracer concentration curve; performing anomaly detection on a plurality of the light intensity curves; Extract abnormal features; Build a dataset for identifying unusual patterns.

6. The method for quantitative analysis of dynamic diffusion of ICG fluorescence in the lungs according to claim 1, characterized in that: Also includes: Analyzing the input lung ICG fluorescence image based on the vascular function evaluation model; Generate pulmonary vascular function assessment report; The pulmonary vascular function assessment report includes vascular perfusion status, abnormal area identification and oxygen supply capacity assessment.

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