Noninvasive portal vein hemodynamic parameter measuring method, equipment and medium

Abstract

The invention discloses a non-invasive portal vein hemodynamic parameter measuring method and device and a medium, and relates to the technical field of medical detection equipment and biomedical signal processing.The method comprises the steps that abdominal epidermal potential distribution data is collected, a conductivity change area is recognized, and a portal vein trunk three-dimensional thermodynamic diagram is generated; peak coordinates of the portal vein trunk three-dimensional thermodynamic diagram are extracted, a portal vein trunk space positioning coordinate set is obtained, a graphene quantum tunneling sensor is driven to be attached to the abdominal skin surface of the patient based on the portal vein trunk space positioning coordinate set, and an attachment ready signal is output; based on the attachment ready signal, an original current data flow is collected, temperature drift correction is performed on the original current data flow, the original current data flow is converted into an instantaneous flow velocity, and a time-flow velocity sequence is generated. The microcosmic dynamic of early portal extravasated blood is captured, the pathological low-speed event which cannot be detected by the traditional technology is identified, and a non-invasive early warning index is provided for the liver cirrhosis progress risk.

Description

Technical Field

[0001] This invention relates to the field of medical testing equipment and biomedical signal processing technology, and in particular to a non-invasive method, equipment and medium for measuring portal vein hemodynamic parameters. Background Technology

[0002] Portal vein hemodynamic assessment is a key method for diagnosing liver diseases such as cirrhosis and portal hypertension. Routine clinical methods include Doppler ultrasound, phase-contrast magnetic resonance imaging (PC-MRI), and invasive hepatic venous pressure gradient (HVPG) measurement. Doppler ultrasound provides non-invasive assessment by detecting blood flow velocity and direction, offering advantages such as ease of operation and real-time performance. PC-MRI provides three-dimensional blood flow vector field information, enabling quantitative flow analysis. HVPG measures the pressure gradient via catheter intervention, but its applicability is limited. Electroelectrical impedance analysis (EIT) technology reconstructs tissue conductivity distribution using an abdominal surface electrode array, aiding in portal vein localization.

[0003] However, existing technologies still have two significant limitations. First, ultrasound and EIT have a large error in locating the portal vein in obese patients (BMI>35). Due to sound wave attenuation and current diffusion effects, the signal-to-noise ratio of deep blood vessels is reduced, and some patients need to be scanned repeatedly. Second, traditional Doppler technology has a low detection rate for low congestion flow velocity. Due to Doppler angle dependence and motion artifact limitations, it cannot capture early portal vein congestion characteristics. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a non-invasive method for measuring portal vein hemodynamic parameters to solve the problems of inaccurate spatial localization of the portal vein and missed detection of ultra-low velocity blood flow in obese patients.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a non-invasive method for measuring portal vein hemodynamic parameters, comprising: acquiring abdominal skin potential distribution data, identifying areas of conductivity variation, and generating a three-dimensional thermogram of the portal vein trunk; extracting the peak coordinates of the three-dimensional thermogram of the portal vein trunk, obtaining a spatial positioning coordinate set of the portal vein trunk, and, based on the spatial positioning coordinate set of the portal vein trunk, driving a graphene quantum tunneling sensor to attach to the patient's abdominal skin surface, and outputting an attachment ready signal; acquiring raw current data stream based on the attachment ready signal, performing temperature drift correction on the raw current data stream, and converting it into instantaneous flow velocity to generate a time-flow velocity sequence; calculating the proportion of ultra-low velocity blood flow based on the time-flow velocity sequence, generating a spatial distribution thermogram, and performing pathological diagnostic marking based on the spatial distribution thermogram to generate a portal vein ultra-low velocity blood flow measurement report.

[0007] In a preferred embodiment of the non-invasive portal vein hemodynamic parameter measurement method of the present invention, the steps for generating a three-dimensional thermogram of the main portal vein are as follows: Based on the tissue dielectric parameter library, Newton's iteration was performed on the abdominal epidermal potential distribution data to generate a conductivity distribution map. Based on the conductivity distribution map, features of high conductivity regions are extracted, and spatial context features of high conductivity regions are integrated to obtain a portal vein binarization mask. Based on the portal vein binarization mask and conductivity distribution map, the main trunk of the portal vein is identified, and the centerline coordinates of the main trunk of the portal vein are extracted to generate a set of portal vein coordinates; The portal vein coordinate set and conductivity distribution map are ray-projected and registered with the standard anatomical coordinate system to generate a three-dimensional thermal map of the main portal vein.

[0008] As a preferred embodiment of the non-invasive portal vein hemodynamic parameter measurement method of the present invention, the acquisition of the spatial positioning coordinate set of the portal vein trunk refers to the identification of spatial points with high thermal intensity quantiles based on the three-dimensional thermal map of the portal vein trunk using the multi-scale local extremum detection method, thereby acquiring the spatial positioning coordinate set of the portal vein trunk.

[0009] In a preferred embodiment of the non-invasive portal vein hemodynamic parameter measurement method of the present invention, the steps for outputting the attachment readiness signal are as follows: Collect reference markers for the robotic arm and combine them with the spatial positioning coordinate set of the main portal vein. Then, convert the coordinate system of the three-dimensional heat map of the main portal vein into the robotic arm operation coordinate system to identify the target coordinates of the robotic arm. The graphene quantum tunneling sensor is driven to move directly above the target coordinates of the robotic arm, applies negative pressure to the surface of the patient's abdominal skin, adjusts the contact pressure to the preset pressure value, and outputs an attachment ready signal.

[0010] As a preferred embodiment of the non-invasive portal vein hemodynamic parameter measurement method of the present invention, the acquisition of raw current data stream refers to: based on the attachment readiness signal, activating the tunneling current monitoring circuit to monitor the tunneling current value in real time, activating the subcutaneous temperature sensor to collect the skin temperature value, and generating raw current data stream.

[0011] As a preferred embodiment of the non-invasive portal vein hemodynamic parameter measurement method of the present invention, the steps for generating the time-flow rate sequence are as follows: Based on the original current data stream, the corrected current change value is calculated using the temperature compensation formula, and temperature abrupt points are marked to generate a temperature-compensated current sequence. Based on a preset velocity conversion coefficient, the corrected current change value is converted into instantaneous velocity, and nonlinear compensation processing is performed on the corrected current change value according to the tunneling current value to generate a time-velocity sequence.

[0012] As a preferred embodiment of the non-invasive portal vein hemodynamic parameter measurement method of the present invention, the steps for calculating the proportion of ultra-low velocity blood flow and generating a spatial distribution heatmap are as follows: Based on time-flow velocity sequences, the duration intervals of blood flow status are identified, and these intervals are segmented into time periods. Simultaneously, the average flow velocity is labeled to generate a time period label set. Based on the time period marker set, ultra-low velocity blood flow time periods are selected according to the ultra-low velocity threshold, and waveform feature analysis is performed to output the effective ultra-low velocity time period set; Based on the effective ultra-low velocity time period set, extract the total duration and total monitoring duration of each effective ultra-low velocity time period, and calculate the proportion of ultra-low velocity blood flow. The location of each effective ultra-low velocity period within the total monitoring duration is converted into temporal spatial coordinates, and a spatial distribution heatmap is generated by rendering using Gaussian kernel density estimation.

[0013] As a preferred embodiment of the non-invasive portal vein hemodynamic parameter measurement method of the present invention, the steps for generating the portal vein ultra-low velocity blood flow measurement report are as follows: Based on the proportion and spatial distribution heatmap of ultra-low velocity blood flow, the preset three-level diagnostic rules are invoked to perform pathological diagnostic marking and generate diagnostic label groups; The diagnostic label group, the proportion of ultra-low velocity blood flow, the spatial distribution heat map, and the spatial positioning coordinate set of the main portal vein are fused to generate a report on the measurement of ultra-low velocity blood flow in the portal vein.

[0014] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the non-invasive portal vein hemodynamic parameter measurement method as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the non-invasive portal vein hemodynamic parameter measurement method as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: by generating a three-dimensional thermal map of the portal vein trunk, sub-millimeter-level spatial positioning of the portal vein trunk is achieved, breaking through the penetration depth limitations of traditional imaging, which is especially suitable for patients with high BMI, providing an anatomical navigation basis for subsequent precise sensor attachment, and avoiding signal distortion caused by positioning deviation; by analyzing the spatiotemporal distribution of ultra-low velocity blood flow, the microscopic dynamics of early portal vein congestion are captured, and pathological low-velocity events that cannot be detected by traditional techniques are identified, providing a non-invasive early warning indicator for the risk of cirrhosis progression. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a non-invasive method for measuring portal vein hemodynamic parameters; Figure 2 A flowchart for generating a three-dimensional thermal map of the main portal vein; Figure 3 A flowchart for portal vein spatial localization and sensor attachment; Figure 4 This is a flowchart for measuring and generating hemodynamic parameters. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Reference Figures 1-4 This is one embodiment of the present invention, which provides a non-invasive method for measuring portal vein hemodynamic parameters, comprising the following steps: S1. Collect abdominal skin potential distribution data, identify areas of conductivity change, and generate a three-dimensional thermal map of the portal vein trunk. Abdominal skin potential distribution data includes inter-electrode voltage amplitude and phase angle; It should be noted that the voltage amplitude between electrodes is the quantized amplitude data output after real-time measurement of the peak potential difference between the two electrodes by a differential amplifier and analog-to-digital conversion by an ADC; the phase angle is the phase offset angle of the fundamental component extracted by performing a fast Fourier transform on the AC voltage signal between electrodes. Based on the tissue dielectric parameter library, Newton's iteration was performed on the abdominal epidermal potential distribution data to generate a conductivity distribution map. Furthermore, based on the abdominal skin potential distribution data, current injection boundary conditions are set, and combined with pre-stored tissue conductivity parameters, abdominal potential distribution data is obtained through the physical relationship of the current field. Subsequently, based on the spatial variation characteristics of the potential distribution data, an abdominal region electric field intensity matrix characterizing the power plant intensity is generated. The abdominal region electric field intensity matrix is ​​combined with the tissue dielectric parameter library to perform dielectric current response simulation and obtain the theoretical current density tensor. The actual skin current value is obtained through a high-precision current probe, and the theoretical current density tensor is compared with the actual skin current value to output a conductivity correction vector. The conductivity correction vector is processed by the Newton-Raphson iteration algorithm to dynamically update the conductivity estimates in the tissue dielectric parameter library, forming an updated tissue dielectric parameter library. The current simulation-data comparison-conductivity correction process is repeated until the error converges. Finally, the conductivity values ​​in the updated tissue dielectric parameter library are mapped to the abdominal three-dimensional spatial grid to generate a high-precision conductivity distribution map. It should be noted that the tissue dielectric parameter library is a physical storage device constructed through in vitro tissue sample measurement, medical image registration, and in vivo impedance scanning, containing numerical tables of conductivity and dielectric constant of muscle / fat / organ tissues; Based on the conductivity distribution map, features of high conductivity regions are extracted, and spatial context features of high conductivity regions are integrated to obtain a portal vein binarization mask. Furthermore, the conductivity distribution map is segmented based on a conductivity threshold to extract high-conductivity regions and generate a binary mask for these regions. Morphological closing operations are then performed on the high-conductivity region binary mask: circular structuring elements are used to fill holes and smooth boundaries, outputting a connected region optimization mask. This optimized mask is then combined with the conductivity distribution map to identify the geometric center coordinates and spatial gradient information of each high-conductivity region, generating a topological correlation matrix between regions. Distance-weighted feature fusion is performed on the topological correlation matrix to generate a spatial context feature tensor, and portal vein anatomical feature matching is performed on this tensor to generate a portal vein candidate region label map. Finally, morphological skeleton extraction is performed on the portal vein candidate region label map, and terminal branches are removed to obtain the final portal vein binary mask. It should be noted that the conductivity threshold is a critical value that distinguishes between high and low conductivity tissues. It is set through in vitro impedance measurement and in vivo imaging calibration, and an exemplary value range is [0.2-0.3]. Based on the portal vein binarization mask and conductivity distribution map, the main trunk of the portal vein is identified, and the centerline coordinates of the main trunk of the portal vein are extracted to generate a set of portal vein coordinates; Furthermore, connected regions in the portal vein binarized mask are extracted and registered with the conductivity distribution map. The average conductivity value of the portal vein trunk candidate region set on the conductivity distribution map is calculated and compared with the conductivity threshold. Regions with conductivity exceeding the threshold are selected to generate a high-confidence region set for the portal vein trunk. Morphological thinning processing (iterative erosion of boundaries until a single-pixel wide centerline is generated) is performed on the high-confidence region set for the portal vein trunk to generate a portal vein trunk skeleton line image. Continuous pixel paths are traced along the portal vein trunk skeleton line on the portal vein trunk skeleton line image, and the two-dimensional coordinates of the path nodes are extracted to generate a portal vein trunk coordinate set. Finally, the portal vein trunk coordinate set is aligned with the spatial coordinate system parameters of the CT image and converted into three-dimensional spatial coordinates through affine transformation to generate a spatially registered portal vein coordinate set. The expression for calculating the average conductivity value is: ; in, This is the average conductivity value; It is the candidate region set for the main portal vein; It is the candidate region set of the main portal vein. The total number of pixels included; These are the pixel coordinates of the candidate region set for the main portal vein. It is a pixel. The conductivity value; It should be noted that the CT image spatial coordinate system parameters are DICOM standard parameters that describe the physical spatial location of the image. They are extracted directly from the metadata of the DICOM file generated by the CT device and include the origin coordinates, pixel spacing, and slice thickness. The portal vein coordinate set and conductivity distribution map are ray-projected and registered with the standard anatomical coordinate system to generate a three-dimensional heat map of the main portal vein. Furthermore, based on the portal vein trunk coordinate set, the three-dimensional spatial position and corresponding conductivity value of each coordinate point are extracted to generate an attributed spatial coordinate dataset. The attributed spatial coordinate dataset is then spatially registered with the spatial coordinate system parameters of the CT image, and an affine transformation is performed to generate an anatomical spatial registration coordinate set. Based on the anatomical spatial registration coordinate set, sampling is performed along the main axis of the portal vein (from the porta hepatis to the perihepatic region) with a fixed step size. At each sampling point, the weighted sum of all conductivity values ​​is accumulated to generate the original voxel intensity distribution field. The original voxel intensity distribution field is subjected to three-dimensional Gaussian filtering to eliminate high-frequency noise. At the same time, trilinear interpolation is used to fill in the empty voxels caused by the sampling gap to generate an optimized voxel intensity field. Finally, the optimized voxel intensity field is rendered as a heatmap: the conductivity value of each voxel is normalized and mapped to the color gamut through HSV-RGB conversion. At the same time, an RGBA semi-transparent gold layer is superimposed on the portal vein binarization mask area, and a three-dimensional heatmap of the portal vein trunk is generated by light projection.

[0023] S2. Extract the peak coordinates of the three-dimensional thermal map of the portal vein trunk, obtain the spatial positioning coordinate set of the portal vein trunk, and drive the graphene quantum tunneling sensor to attach to the patient's abdominal skin surface based on the spatial positioning coordinate set of the portal vein trunk, and output the attachment ready signal. Based on the three-dimensional thermal map of the main portal vein, a multi-scale local extremum detection method is used to identify spatial points with high thermal intensity quantiles and obtain the spatial positioning coordinate set of the main portal vein. Furthermore, based on the 3D heatmap of the portal vein trunk, an unsigned integer array of pixel data labels is extracted, normalized to grayscale values ​​through linear transformation of window width and window level, and reorganized into a 3D matrix according to row and column order. Simultaneously, a spatial coordinate mapping relationship (physical origin + direction matrix + voxel spacing) is bound to generate an original voxel matrix. A three-level sliding window extreme value detection is then performed on the original voxel matrix: first, a 3×3×3 window is used to identify local maxima; second, a 5×5×5 window is used to verify the spatial continuity of the local maxima (removing isolated points); finally, a 7×7×7 window is used to mark the spatially continuous verified local maxima and remove artifacts, generating a candidate extreme value set. All thermal intensity values ​​within the candidate extreme value set are then... After sorting, the high quantile values ​​are taken as the high intensity quantile threshold, and points exceeding the high intensity quantile threshold are selected from the candidate extreme point set to generate a high confidence hotspot set. Based on the spatial density difference of the high confidence hotspot set, noise points are separated, and the geometric morphological features of each cluster are identified. The cluster with the most significant spatial extension and the densest point clustering is marked as the main cluster (labeled "trunk"). Then, the spatial connection continuity between neighboring clusters and the main trunk is checked. Those with direct connection relationships are marked as sub-clusters (labeled "lateral branches"). A clustering label coordinate set is generated, and based on the clustering label coordinate set, the point set labeled "trunk" is selected. At the same time, the centroid coordinates of the cross section are identified at fixed intervals along the main axis of the portal vein (from the porta hepatis to the perihepatic area), and finally, the spatial positioning coordinate set of the main portal vein is generated. It should be noted that the high intensity quantile threshold is a dynamic cutoff value obtained by sorting the thermal intensity values ​​of the candidate extreme point set and taking the high quantile. An exemplary value range is [0.85-0.95]. The cross-sectional centroid coordinates are obtained by intercepting the vertical plane at fixed intervals along the main axis of the portal vein, querying all vascular point groups in the vertical plane, and identifying the spatial geometric center point of all vascular point groups. Collect reference markers for the robotic arm and combine them with the spatial positioning coordinate set of the main portal vein. Then, convert the coordinate system of the three-dimensional heat map of the main portal vein into the robotic arm operation coordinate system to identify the target coordinates of the robotic arm. Furthermore, firstly, the spatial positions of three infrared reflective markers installed at the end of the robotic arm are acquired using an optical tracker to generate a reference marker coordinate set. This reference marker coordinate set is then spatially registered with the spatial positioning coordinate set of the portal vein trunk. Simultaneously, an optimal rigid body transformation is used to generate a spatial registration matrix. Next, based on the spatial registration matrix, a global affine transformation is performed on the coordinate system of the three-dimensional heat map of the portal vein trunk to convert it into the robotic arm's operating coordinate system. In the converted robotic arm operating coordinate system, the spatial positioning coordinate set of the portal vein trunk is mapped using the same spatial registration matrix to generate a registered coordinate set. Finally, the registered coordinate set is combined with the robotic arm's kinematic parameters and the environmental obstacle map to perform collision-free trajectory planning and output the target coordinates of the robotic arm. It should be noted that the kinematic parameters of the robotic arm are obtained by defining the physical dimensions of each joint of the robotic arm and the motion constraints of the end effector through the definition of the robotic arm configuration. These parameters include joint length, joint rotation range, and end effector posture matrix, and are used to describe the motion constraint relationship of the robotic arm. The environmental obstacle map is a spatial constraint data generated based on the three-dimensional segmentation results of the patient's preoperative CT images. It includes the coordinate range of the rib projection area, the three-dimensional coordinate point set of the organ boundary, and the equipment safety distance markers. This data is used to define the obstacle avoidance area for the robotic arm movement. The graphene quantum tunneling sensor is driven to move directly above the target coordinates of the robotic arm, applies negative pressure to the surface of the patient's abdominal skin, adjusts the contact pressure to the preset pressure value, and outputs an attachment ready signal. Furthermore, the robotic arm controller receives the target coordinate sequence of the robotic arm, drives the six-axis joint to move directly above the first target point, and the laser rangefinder verifies the positioning error in real time, outputting a precise positioning completion signal. Based on the precise positioning completion signal, the vacuum pump is triggered to start, and the silicone layer of the sensor substrate forms an airtight adsorption with the skin surface. The pressure sensor array monitors the initial adsorption pressure in real time and outputs an adsorption status confirmation signal. Upon receiving the adsorption status confirmation signal, the vacuum valve opening is dynamically adjusted according to the difference between the initial adsorption pressure and the preset pressure value (0.3 N / cm²) until the pressure across the entire area stabilizes, and an attachment ready signal is output. It should be noted that the preset pressure value is an optimized parameter determined through in vitro skin blood flow coupling experiments and clinical verification. It represents the optimal pressure value obtained through a large number of clinical trials under the dual constraints of ensuring tight coupling between the sensor microcavity and the skin without compressing subcutaneous blood vessels.

[0024] S3. Based on the attachment readiness signal, acquire the raw current data stream, perform temperature drift correction on the raw current data stream, and convert it into instantaneous flow rate to generate a time-flow rate sequence. Based on the attachment readiness signal, the tunneling current monitoring circuit is activated to monitor the tunneling current value in real time, and the subcutaneous temperature sensor is activated to collect the skin temperature value and generate the raw current data stream. Furthermore, based on the attachment ready signal, the tunneling current monitoring circuit is activated to apply a bias voltage to the graphene quantum dot layer, and the raw value of the tunneling current is collected in real time, outputting a raw current value sequence. Based on the attachment ready signal, the subcutaneous temperature sensor is activated simultaneously to collect the temperature value of the skin contact interface, outputting a temperature value sequence. The raw current value sequence and the temperature value sequence are aligned according to the timestamp, and the current value of a fixed time period (e.g., a 100ms time period) is paired with the temperature value at the corresponding time to generate a raw current data stream. It should be noted that the graphene quantum dot layer is the core functional layer of the quantum tunneling sensor. It is a nanostructure layer prepared by laser ablation-assisted chemical synthesis. After surface modification with liver sinusoidal endothelial-specific antibodies, it is spin-coated and solidified onto the sensor microcavity array substrate to form an electron tunneling sensitive interface. Based on the original current data stream, the corrected current change value is calculated using the temperature compensation formula, and temperature abrupt points are marked to generate a temperature-compensated current sequence. The expression for calculating the corrected current change is: ; in, This is the corrected current change value; It is the original tunneling current change value, representing the instantaneous fluctuation of the quantum tunneling current without temperature compensation; It is a dynamic temperature compensation coefficient (set through in vitro blood flow calibration and extensive clinical validation; an exemplary value range is [0.018-0.026]). This is the deviation between the current temperature and the reference temperature; This is the current temperature; Furthermore, based on the temperature value sequence in the original data stream, the temperature change is identified point by point using the first point temperature as the reference temperature, generating a temperature change sequence. A temperature compensation coefficient is dynamically loaded according to the absolute value range of the temperature change sequence, generating a temperature compensation coefficient sequence. Based on the temperature compensation coefficient sequence, a temperature compensation formula is applied to each current value in the original data stream to calculate the corrected current change value, generating a corrected current change value sequence. The rate of temperature change is detected in real time in the temperature change sequence. When the rate of temperature change of two consecutive points exceeds the temperature abrupt change threshold, it is marked as the abrupt change start point; when it returns to within the temperature steady-state threshold, it is marked as the abrupt change end point, generating a temperature change sequence with abrupt change markers. Finally, the corrected current change value sequence and the temperature change sequence with abrupt change markers are aligned by timestamps to generate a temperature-compensated current sequence. It should be noted that the temperature mutation threshold is a critical value determined through clinical blood flow signal distortion analysis. When the rate of temperature change exceeds the temperature mutation threshold, the blood flow signal is distorted. An exemplary value range is [0.48-0.52]. The temperature steady-state threshold is a critical value set based on the principle of thermal noise equivalence. When the rate of temperature change is lower than the temperature steady-state threshold, the corresponding blood flow signal fluctuation meets clinical requirements. An exemplary value range is [0.08-0.12]. Based on a preset velocity conversion coefficient, the corrected current change value is converted into instantaneous velocity, and nonlinear compensation processing is performed on the corrected current change value according to the tunneling current value to generate a time-velocity sequence. The expression for calculating instantaneous flow velocity is: ; in, It is the instantaneous flow rate; This is the current-to-flow-rate conversion gain coefficient (verified and set through in vitro flow channel experiments and numerous comparative ultrasound vector imaging experiments; an exemplary value range is [3.0-3.4×10⁻⁶]). -6 ]); This is the corrected current change value; Furthermore, based on the corrected current change value in the temperature-compensated current sequence, the instantaneous flow velocity is calculated using a linear conversion formula by calling a preset flow velocity conversion coefficient, generating an initial flow velocity sequence. The initial flow velocity sequence is then scanned to extract all data points whose instantaneous flow velocity exceeds the flow velocity threshold, and these data points are marked as nonlinear segments, generating a nonlinear segment marker sequence. For the data points covered by the nonlinear segment marker sequence, the corresponding segmented compensation rules are applied based on the preset current interval where the corrected current change value is located, generating a compensated flow velocity sequence. The initial flow velocity sequence and the compensated flow velocity sequence are then merged by timestamp, and outliers are removed to generate an optimized flow velocity sequence. Finally, the optimized flow velocity sequence is aligned and encapsulated with the original timestamp to generate a time-flow velocity sequence. It should be noted that the flow velocity conversion coefficient is a current-flow velocity linear conversion constant determined through in vitro simulated portal vein blood flow experiments and comparative verification with a large number of clinical gold standards (ultrasound vector imaging). An exemplary value range is [3.0-3.4×10]. 5 The flow rate threshold is a critical value determined by the physical response characteristics of the quantum tunneling sensor and extensive clinical validation, with an exemplary range of [19.5-20.5]. The current range is a range determined by the nonlinear response characteristics of the quantum tunneling sensor and extensive clinical validation, specifically divided into two ranges: 5-7nA and >7nA. The segmented compensation rule is a slope coefficient adjustment rule based on current range matching, including: a slope coefficient of 0.92 is applied to the 5-7nA range, and a slope coefficient of 0.85 is applied to the range exceeding 7nA.

[0025] S4. Based on the time-flow velocity sequence, calculate the proportion of ultra-low velocity blood flow, generate a spatial distribution heat map, and perform pathological diagnostic markings based on the spatial distribution heat map to generate a portal vein ultra-low velocity blood flow measurement report.

[0026] Based on time-flow velocity sequences, the duration intervals of blood flow status are identified, and these intervals are segmented into time periods. Simultaneously, the average flow velocity is labeled to generate a time period label set. The expression for calculating the average flow velocity is: ; in, It is the first Average flow rate over a period of time; It is the first The duration of each period; It is the start timestamp of the time period; It is the end timestamp of the time period; It is the sampling time point; It is a time-flow velocity function (principle: based on the quantum tunneling effect, the blood flow velocity perturbs the microcavity barrier of the graphene quantum dot layer, causing a linear change in the tunneling current, and a continuous flow velocity time function is generated after temperature compensation and time-domain sampling). It is the time period index number; It is the sampling time interval; Furthermore, based on the velocity values ​​in the time-velocity sequence, the velocity change trend in the time-velocity sequence is analyzed through a sliding window to identify the direction and intensity of velocity change, generate a velocity change rate sequence, and scan the velocity change rate sequence to identify time periods (steady-state blood flow) where the absolute value of the continuous rate of change is less than the blood flow steady-state determination threshold. At the same time, the start and end timestamps are marked to generate a blood flow state duration interval set. The blood flow state duration interval set is divided into sub-time periods with the zero-crossing point of the velocity change rate as the dividing point. Adjacent small intervals with an interval less than the interval threshold are merged to generate an optimized time period segmentation sequence. The average velocity is calculated for each time period in the optimized time period segmentation sequence and labeled as the time period average velocity value to generate a time period sequence with average velocity labeling. Finally, the time period sequence with average velocity labeling is encapsulated into a structure array to generate a time period label set. It should be noted that the blood flow steady state determination threshold was determined through verification using the gold standard of ultrasound vector imaging in a large number of cirrhotic patients. When the rate of change of flow velocity exceeds the blood flow steady state determination threshold, it indicates that blood flow is not in steady state. An exemplary value range is [0.48-0.52]. The interval threshold is the minimum common period component set based on the respiratory motion artifact cycle and cardiac pulsation interference. An exemplary value range is [45-55]. Based on the time period marker set, ultra-low velocity blood flow time periods are selected according to the ultra-low velocity threshold, and waveform feature analysis is performed to output the effective ultra-low velocity time period set; Furthermore, based on the time period marker set, all time periods with average flow velocities below the ultra-low velocity threshold are screened, and the time period ID, start and end timestamps, and average flow velocity values ​​are extracted to generate an ultra-low velocity time period candidate set. For each time period in the ultra-low velocity time period candidate set, the flow velocity value of the original time-flow velocity sequence is extracted, and the waveform flatness index (the standard deviation of the flow velocity difference between adjacent points) is calculated. When the waveform flatness index is lower than the laminar flow steady-state threshold (the upper limit of the standard deviation of blood flow velocity fluctuation determined through eddy noise critical value experiments and a large amount of clinical data, with an exemplary value range of [0-0.15]), it is marked as a valid flat segment, generating a flat waveform marker set. The start and end timestamps of the time periods in the flat waveform marker set are compared with the respiratory tract flow threshold. The respiratory motion monitoring signal is aligned along the time axis. The respiratory waveform is stretched / compressed to the same duration using a dynamic time warping algorithm. The cross-correlation matrix of the stretched / compressed respiratory waveform is identified, and the maximum value of the main diagonal region in the cross-correlation matrix is ​​extracted as the time-domain correlation index. If the time-domain correlation index is lower than the respiratory artifact threshold (the critical value of the similarity between respiratory motion and blood flow signal waveforms set based on the time-domain correlation analysis of respiratory motion and blood flow signal interference, with an exemplary value range of [0.3-1.0], when the time-domain correlation index exceeds the respiratory artifact threshold, respiratory artifacts are likely to cause flow velocity distortion), the time period is determined to be free of artifact interference. Finally, the effective ultra-low velocity time period set free of artifact interference is output. The expression for calculating the waveform flatness index is: ; It should be noted that, It is the first Waveform flatness index for each time period; It is the change in flow velocity between adjacent points; It is the first Instantaneous flow rate at each sampling moment; It is the average change in flow velocity over a period of time; It is the first The total number of sampling points in each time period; It is the discrete sampling point number, identifying the first... Each sampling time; It should be noted that the ultra-low velocity threshold is a critical value determined through verification using the gold standard of portal vein congestion in a large number of patients with cirrhosis, with an exemplary range of [0.95-1.05]. The respiratory motion monitoring signal is a chest wall displacement acceleration signal acquired in real time by a triaxial MEMS accelerometer attached 2 cm below the patient's xiphoid process. The respiratory rhythm waveform is extracted by bandpass filtering and used to characterize the phase and amplitude of respiratory motion. Based on the effective ultra-low velocity time period set, extract the total duration and total monitoring duration of each effective ultra-low velocity time period, and calculate the proportion of ultra-low velocity blood flow. The expression for calculating the proportion of ultra-low velocity blood flow is: ; It should be noted that, It is the proportion of ultra-low velocity blood flow; It is the duration of the effective ultra-low speed period; It is an effective ultra-low speed period Total duration; It is the total amount over a period of time; This is the total monitoring duration; It is the time period index number; Furthermore, iterate through each time period in the effective ultra-low velocity time period set, extract the start and end timestamps of each time period, obtain the duration, accumulate the durations of all time periods, generate the total duration value of the ultra-low velocity time period, and at the same time read the total detection duration (the baseline time generated and locked when detection is started through the fixed time parameters set by the blood flow monitoring circuit). Perform a ratio calculation between the total duration value of the ultra-low velocity time period and the total monitoring duration to calculate the initial ultra-low velocity blood flow ratio; verify the initial ultra-low velocity blood flow ratio (for example, whether it is within the reasonable range of 0-100%; if it exceeds the reasonable range, whether the detection time period timestamp overflows), and finally output the verified ultra-low velocity blood flow ratio. The location of each effective ultra-low velocity period within the total monitoring time is converted into temporal spatial coordinates, and rendered using Gaussian kernel density estimation to generate a spatial distribution heatmap. Furthermore, the process iterates through each time period in the effective ultra-low velocity time period set, identifies the center point time coordinates of each time period, generates a time period center point coordinate set, and converts the absolute timestamps in the time period center point coordinate set into relative position coordinates based on the total monitoring duration, generating a relative position coordinate set. Based on the distribution of the relative position coordinate set on the time axis, a kernel density estimation sequence is generated through a preset density weight allocation rule, and the kernel density estimation sequence is then thermally rendered using a hue mapping rule to generate a thermal rendering data package. Finally, the thermal rendering data package is encoded and bound to the patient ID and monitoring timestamp to generate a spatial distribution heatmap. It should be noted that the density weighting allocation rule is a spatiotemporal aggregation quantification strategy developed through extensive clinical gold standard validation, including: a weight decay coefficient (set through clinically measured distance-weight decay curve, with an exemplary value range of 0.48-0.52), a fixed bandwidth value, and a boundary compensation factor (set based on the event detection rate of the first and last regions of the time axis, with an exemplary value range of 1.15-1.25); the hue mapping rule is a density-color correspondence determined through clinical visual optimization experiments, including density value critical point segmentation, a blue-yellow-red three-order gradient color spectrum, and a forced gold rendering mechanism; Based on the proportion and spatial distribution heatmap of ultra-low velocity blood flow, the preset three-level diagnostic rules are invoked to perform pathological diagnostic marking and generate diagnostic label groups; Furthermore, the proportion of ultra-low velocity blood flow is matched with the three-level threshold rules for portal vein congestion to generate a primary diagnostic label. Based on the nuclear density estimate and high-density aggregation threshold in the spatial distribution heatmap, high-density aggregation areas (focal / diffuse) are identified, generating heatmap feature markers. The primary diagnostic label and heatmap feature markers are matched with pathological risk grading rules (for example, if the primary label is "severe congestion" and the heatmap feature marker is "focal high density," then "high risk of cirrhosis progression" is marked) to generate a secondary diagnostic label. The secondary diagnostic label is matched with the guideline rule base to associate with complication risks (e.g., "high risk of cirrhosis progression" corresponds to "endoscopic follow-up within 6 weeks") to generate a clinical intervention recommendation label. The primary diagnostic label, secondary diagnostic label, and clinical intervention recommendation label are integrated and encapsulated to generate a diagnostic label group. It should be noted that the high-density aggregation threshold is a critical value determined through numerous clinical controlled studies on the relationship between thermographic signal intensity and pathological grading of cirrhotic nodules, with an exemplary range of [0.68-0.72]. The portal vein congestion three-level threshold rule is a hemodynamic-pathological correlation standard developed through numerous multicenter clinical trials, used to quantitatively assess the severity of portal vein congestion and the risk of complications. The specific rules are as follows. If the percentage of ultra-low velocity blood flow is greater than the threshold for mild congestion (a critical value determined by a baseline study of the percentage of ultra-low velocity blood flow in the portal vein of healthy individuals, used to distinguish between physiological fluctuations and early congestion, with an exemplary range of [15.01%-24.99%]), it is marked as "mild congestion"; If the percentage of ultra-low velocity blood flow is greater than the threshold for severe congestion (the cutoff point for progressive congestion determined based on follow-up data of patients with compensated cirrhosis and ROC curve validation, with an exemplary range of [25.01%~34.99%]), it is marked as "severe congestion"; The proportion of ultra-low velocity blood flow > the critical congestion threshold (based on the criteria for critical congestion confirmed by clinical studies and multicenter validation in patients with decompensated cirrhosis; an exemplary range of values ​​is...). >35%), marked as "critical hematoma"; It should be noted that the pathological risk stratification rules are a two-dimensional decision matrix validated through numerous multicenter clinical trials. The matrix uses primary diagnostic labels (mild congestion / severe congestion / critical congestion) as rows and heatmap feature markers (focal high density / diffuse high density / no aggregation) as columns. Intersections define secondary diagnostic labels (e.g., "severe congestion + focal high density → high risk of cirrhosis"), used to fuse multimodal features to achieve risk stratification. The guideline rule base is a key-value mapping table digitally generated based on the AASLD 2023 clinical guidelines, containing secondary diagnostic labels, corresponding clinical intervention recommendations, and applicable parameters, used to map secondary diagnostic labels to specific clinical intervention recommendations. The diagnostic label group, the proportion of ultra-low velocity blood flow, the spatial distribution heat map, and the spatial positioning coordinate set of the main portal vein are fused to generate a report on the measurement of ultra-low velocity blood flow in the portal vein. Furthermore, the primary and secondary diagnostic labels and clinical intervention recommendations in the diagnostic label group are marked, and the text fields are filled according to the clinical report template to generate a structured diagnostic conclusion text. A natural language description is generated based on the spatial location coordinate set of the portal vein trunk, matched with the CT anatomy atlas library, and a spatial location description set is output. The spatial distribution heatmap and the spatial location description set are aligned through coordinate registration, and gold cross markers and location labels are superimposed on the corresponding coordinate points to generate a heatmap with spatial markings. Features of the ultra-low velocity blood flow proportion and time period marker set (such as the longest duration) are extracted, and the data is filled according to a standard table template to generate a core parameter table. Finally, the structured diagnostic conclusion text, the spatially marked heatmap, the core parameter table, and the patient's basic information are integrated to generate a portal vein ultra-low velocity blood flow measurement report. It should be noted that the CT anatomy atlas library is a three-dimensional spatial coordinate database constructed by experts through the annotation of a large number of high-resolution enhanced CT images of the liver. It includes the voxel coordinate set of liver segment boundaries, the topological relationship of blood vessel course, and the mapping rules of organ spatial location.

[0027] This embodiment also provides a computer device applicable to the non-invasive portal vein hemodynamic parameter measurement method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the non-invasive portal vein hemodynamic parameter measurement method proposed in the above embodiment.

[0028] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0029] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for non-invasive portal vein hemodynamic parameter measurement as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0030] In summary, this invention achieves sub-millimeter-level spatial localization of the portal vein trunk by generating a three-dimensional thermal map, breaking through the penetration depth limitations of traditional imaging. This is particularly suitable for patients with high BMI, providing an anatomical navigation basis for precise sensor attachment and avoiding signal distortion caused by positioning deviations. Furthermore, by analyzing the spatiotemporal distribution of ultra-low-velocity blood flow, it captures the microscopic dynamics of early portal vein congestion, identifies pathological low-velocity events that cannot be detected by traditional techniques, and provides a non-invasive early warning indicator for the risk of cirrhosis progression.

[0031] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A non-invasive method for measuring portal vein hemodynamic parameters, characterized in that: include, Data on the distribution of abdominal skin potential were collected, areas of conductivity variation were identified, and a three-dimensional thermal map of the main portal vein was generated. The peak coordinates of the three-dimensional thermal map of the main portal vein are extracted to obtain the spatial positioning coordinate set of the main portal vein. Based on the spatial positioning coordinate set of the main portal vein, the graphene quantum tunneling sensor is driven to attach to the patient's abdominal skin surface and an attachment ready signal is output. Based on the attachment readiness signal, the raw current data stream is acquired, and the raw current data stream is corrected for temperature drift and converted into instantaneous flow rate to generate a time-flow rate sequence. Based on the time-flow velocity sequence, the proportion of ultra-low velocity blood flow is calculated, a spatial distribution heat map is generated, and pathological diagnostic markings are performed based on the spatial distribution heat map to generate a portal vein ultra-low velocity blood flow measurement report.

2. The non-invasive method for measuring portal vein hemodynamic parameters according to claim 1, characterized in that: The steps for generating a three-dimensional thermal map of the portal vein trunk are as follows: Based on the tissue dielectric parameter library, Newton's iteration was performed on the abdominal epidermal potential distribution data to generate a conductivity distribution map. Based on the conductivity distribution map, features of high conductivity regions are extracted, and spatial context features of high conductivity regions are integrated to obtain a portal vein binarization mask. Based on the portal vein binarization mask and conductivity distribution map, the main trunk of the portal vein is identified, and the centerline coordinates of the main trunk of the portal vein are extracted to generate a set of portal vein coordinates; The portal vein coordinate set and conductivity distribution map are ray-projected and registered with the standard anatomical coordinate system to generate a three-dimensional thermal map of the main portal vein.

3. The non-invasive method for measuring portal vein hemodynamic parameters according to claim 1, characterized in that: The acquisition of the spatial positioning coordinate set of the portal vein trunk refers to the process of identifying spatial points with high thermal intensity quantiles based on the three-dimensional heat map of the portal vein trunk using a multi-scale local extremum detection method, thereby acquiring the spatial positioning coordinate set of the portal vein trunk.

4. The non-invasive method for measuring portal vein hemodynamic parameters according to claim 3, characterized in that: The steps for outputting the attachment ready signal are as follows: Collect reference markers for the robotic arm and combine them with the spatial positioning coordinate set of the main portal vein. Then, convert the coordinate system of the three-dimensional heat map of the main portal vein into the robotic arm operation coordinate system to identify the target coordinates of the robotic arm. The graphene quantum tunneling sensor is driven to move directly above the target coordinates of the robotic arm, applies negative pressure to the surface of the patient's abdominal skin, adjusts the contact pressure to the preset pressure value, and outputs an attachment ready signal.

5. The non-invasive method for measuring portal vein hemodynamic parameters according to claim 1, characterized in that: The acquisition of raw current data stream refers to activating the tunneling current monitoring circuit to monitor the tunneling current value in real time based on the attachment readiness signal, starting the subcutaneous temperature sensor to collect the skin temperature value, and generating the raw current data stream.

6. The non-invasive method for measuring portal vein hemodynamic parameters according to claim 5, characterized in that: The steps for generating the time-flow rate sequence are as follows: Based on the original current data stream, the corrected current change value is calculated using the temperature compensation formula, and temperature abrupt points are marked to generate a temperature-compensated current sequence. Based on a preset velocity conversion coefficient, the corrected current change value is converted into instantaneous velocity, and nonlinear compensation processing is performed on the corrected current change value according to the tunneling current value to generate a time-velocity sequence.

7. The non-invasive method for measuring portal vein hemodynamic parameters according to claim 1, characterized in that: The steps for calculating the proportion of ultra-low velocity blood flow and generating a spatial distribution heatmap are as follows. Based on time-flow velocity sequences, the duration intervals of blood flow status are identified, and these intervals are segmented into time periods. Simultaneously, the average flow velocity is labeled to generate a time period label set. Based on the time period marker set, ultra-low velocity blood flow time periods are selected according to the ultra-low velocity threshold, and waveform feature analysis is performed to output the effective ultra-low velocity time period set; Based on the effective ultra-low velocity time period set, extract the total duration and total monitoring duration of each effective ultra-low velocity time period, and calculate the proportion of ultra-low velocity blood flow. The location of each effective ultra-low velocity period within the total monitoring duration is converted into temporal spatial coordinates, and a spatial distribution heatmap is generated by rendering using Gaussian kernel density estimation.

8. The non-invasive method for measuring portal vein hemodynamic parameters according to claim 7, characterized in that: The steps for generating the portal vein ultra-low velocity blood flow measurement report are as follows. Based on the proportion and spatial distribution heatmap of ultra-low velocity blood flow, the preset three-level diagnostic rules are invoked to perform pathological diagnostic marking and generate diagnostic label groups; The diagnostic label group, the proportion of ultra-low velocity blood flow, the spatial distribution heat map, and the spatial positioning coordinate set of the main portal vein are fused to generate a report on the measurement of ultra-low velocity blood flow in the portal vein.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the non-invasive portal vein hemodynamic parameter measurement method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the non-invasive portal vein hemodynamic parameter measurement method according to any one of claims 1 to 8.

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