Monitoring system and method for vascular ultrasonic guided waves

Abstract

The invention discloses a monitoring system and method for vascular ultrasonic guided waves, and relates to the technical field of image processing, comprising the steps of collecting and processing vascular ultrasonic guided wave monitoring data, analyzing the data, comprehensively analyzing and optimizing and adjusting. The blood vessel ultrasonic guided wave monitoring data are collected and processed, the blood vessel ultrasonic guided wave monitoring data are analyzed, the brainstem hemorrhage image data evaluation value and the brainstem hemorrhage image quality evaluation value are obtained, comprehensive analysis and optimization adjustment are carried out, the accuracy of the blood vessel ultrasonic guided wave monitoring system is improved, and the problem that in the prior art, the blood vessel ultrasonic guided wave monitoring data cannot be accurately analyzed is solved. And the accuracy of a monitoring system of the blood vessel ultrasonic guided wave is insufficient.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a monitoring system and method for blood vessel ultrasonic guided waves. Background Art

[0002] Before the clinical application of CT technology, the diagnosis of brainstem hemorrhage relied primarily on clinical manifestations and auxiliary examinations, such as ventriculography and transcarotid cerebral angiography. These methods have limited diagnostic accuracy and are invasive and risky. The introduction of CT technology has enabled the precise location and size of brainstem hemorrhage, creating the basic conditions for surgical treatment. Existing imaging technologies may not provide clear image details when displaying tiny blood vessels, small lesions, or plaques, which may lead to missed or misdiagnosis of tiny lesions, affecting early diagnosis and treatment effectiveness.

[0003] The existing monitoring system for vascular ultrasonic guided waves uses the vascular guided wave elastic imaging method to implement the finite deformation theory of continuous medium mechanics, and derives the guided wave dispersion equation that takes into account the influence of blood pressure changes, perivascular tissue, etc.; on the basis of traditional ultrasonic detection methods through ultrasonic imaging detection technology, laser ultrasound has become an important detection method due to its non-contact characteristics.

[0004] For example, patent publication number CN110415248B discloses an ultrasound-based vascular monitoring method, apparatus, device, and storage medium, comprising: obtaining a current ultrasound image of a subject's blood vessels; inputting the current ultrasound image into a pre-trained vascular recognition model to determine a tissue contour image of the blood vessels; and determining vascular information of the subject based on the tissue contour image. This embodiment of the present invention identifies the tissue contours of blood vessels in ultrasound images to determine the subject's vascular information, resolving the need for manual differentiation of vascular tissue contours and enabling intelligent monitoring of vascular parameter information.

[0005] For example, the method and device for segmenting the inner and outer membranes of intravascular ultrasound images disclosed in the patent application with announcement number CN104504689B include: inputting the intravascular ultrasound image to be tested into a feature detection model to obtain feature information of each pixel of the intravascular ultrasound image to be tested, wherein the feature detection model is obtained by learning the segmented intravascular ultrasound image samples through an unsupervised learning model; inputting the feature information of each pixel of the intravascular ultrasound image to be tested into a classification model to obtain classification information of the feature information of each pixel of the intravascular ultrasound image to be tested, wherein the classification model is obtained by statistically analyzing the feature information of each pixel of the image sample using a supervised classification method; establishing a dynamic contour model of the blood vessel based on the feature information and classification information of each pixel of the intravascular ultrasound image to be tested, and obtaining a segmentation curve of the inner and outer membranes of the blood vessel.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] The vascular ultrasonic guided wave monitoring system is used for high-resolution magnetic resonance vascular wall imaging. In brainstem hemorrhage images, due to limited penetration depth, there is a problem of insufficient clarity of cardiovascular and cerebrovascular images. Due to limited spatial resolution and insufficient contrast resolution, small lesions or plaques are not clearly displayed on the image and plaques on the vascular wall are difficult to distinguish. Due to image noise and artifacts, the image becomes blurred or distorted, affecting the image quality. There is a problem of insufficient accuracy of the vascular ultrasonic guided wave monitoring system. Summary of the Invention

[0008] The embodiments of the present application provide a monitoring system and method for vascular ultrasonic guided waves, thereby solving the problem of insufficient accuracy of vascular ultrasonic guided wave monitoring systems in the prior art and improving the accuracy of vascular ultrasonic guided wave monitoring systems.

[0009] An embodiment of the present application provides a monitoring system for vascular ultrasonic guided waves, comprising: a vascular ultrasonic guided wave monitoring data acquisition and processing module, a vascular ultrasonic guided wave monitoring data analysis module, a comprehensive analysis module, and an optimization and adjustment module: wherein the vascular ultrasonic guided wave monitoring data acquisition and processing module is used to acquire and process vascular ultrasonic guided wave monitoring data; the vascular ultrasonic guided wave monitoring data analysis module is used to analyze the vascular ultrasonic guided wave monitoring data to obtain a brainstem hemorrhage image data evaluation value and a brainstem hemorrhage image quality evaluation value; the comprehensive analysis module is used to obtain a vascular ultrasonic guided wave monitoring accuracy evaluation value through a comprehensive analysis of the brainstem hemorrhage image quality evaluation value and the brainstem hemorrhage image data evaluation value; the optimization and adjustment module is used to compare and analyze the brainstem hemorrhage image data evaluation value, the brainstem hemorrhage image quality evaluation value, and the vascular ultrasonic guided wave monitoring accuracy evaluation value with a first judgment value of the brainstem hemorrhage image data evaluation value, a second judgment value of the brainstem hemorrhage image quality evaluation value, and a third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value, to obtain a vascular ultrasonic guided wave monitoring optimization and adjustment method.

[0010] Furthermore, the specific steps of collecting and processing vascular ultrasonic guided wave monitoring data are: collecting vascular ultrasonic guided wave monitoring raw data through intravascular ultrasonic diagnostic equipment; cleaning and denoising the vascular ultrasonic guided wave monitoring raw data to obtain vascular ultrasonic guided wave monitoring data; the vascular ultrasonic guided wave monitoring data includes brainstem hemorrhage image data and brainstem hemorrhage image quality data.

[0011] Furthermore, the specific steps of obtaining the brainstem hemorrhage image data evaluation value are as follows: the brainstem hemorrhage image data includes brainstem hemorrhage image voxels, scanning time, and brainstem hemorrhage image imaging speed; obtaining the brainstem hemorrhage image voxel threshold, scanning time threshold, brainstem hemorrhage image imaging speed standard value, brainstem hemorrhage image voxel weight factor, scanning time weight factor, and brainstem hemorrhage image imaging speed weight factor from the vascular ultrasonic guided wave monitoring database; recording the ratio of the brainstem hemorrhage image voxel at the brainstem hemorrhage image data detection point to the brainstem hemorrhage image voxel threshold as the first component of the brainstem hemorrhage image data evaluation value ; The ratio results of the scanning time and the scanning time threshold of different brainstem hemorrhage imaging data detection segments are averaged and recorded as the second component of the brainstem hemorrhage imaging data evaluation value; the standard value of the brainstem hemorrhage image imaging speed and the degree to which the brainstem hemorrhage image imaging speed deviates from the standard value are processed by ratio, and the result obtained by the ratio processing is recorded as the third component of the brainstem hemorrhage image data evaluation value; the brainstem hemorrhage image data evaluation value represents the result of quantifying the brainstem hemorrhage related information by the first component of the brainstem hemorrhage image data evaluation value, the second component of the brainstem hemorrhage image data evaluation value and the third component of the brainstem hemorrhage image data evaluation value.

[0012] Furthermore, the specific process of obtaining the image quality evaluation value of brainstem hemorrhage is as follows: the image quality data of brainstem hemorrhage includes brainstem hemorrhage image voxels, average pixel intensity of the brainstem hemorrhage image surrounding tissue area, average pixel intensity of the brainstem hemorrhage image surrounding tissue area and magnetic resonance signal intensity; the brainstem hemorrhage image voxel threshold, brainstem hemorrhage image vascular area noise standard value, brainstem hemorrhage image surrounding tissue area noise standard value, magnetic resonance signal intensity threshold, contrast-to-noise ratio coefficient weight factor, brainstem hemorrhage image voxel weight factor and magnetic resonance signal intensity weight factor are obtained from the vascular ultrasonic guided wave monitoring database; the average pixel intensity of the brainstem hemorrhage image surrounding tissue area and the brainstem hemorrhage image are combined into a single matrix. The degree to which the average pixel intensity of the surrounding tissue area deviates from the standard value is processed as a proportion and recorded as the contrast-to-noise ratio coefficient; the ratio of the brainstem hemorrhage image voxel threshold and the brainstem hemorrhage image voxel at the brainstem hemorrhage image quality detection point is recorded as the second component of the brainstem hemorrhage image quality assessment value; the ratio of the magnetic resonance signal intensity at the brainstem hemorrhage image quality detection point to the magnetic resonance signal intensity threshold is recorded as the third component of the brainstem hemorrhage image quality assessment value; the brainstem hemorrhage image quality assessment value represents the effect of the contrast-to-noise ratio coefficient, the second component of the brainstem hemorrhage image quality assessment value, and the third component of the brainstem hemorrhage image quality assessment value on quantifying the image quality of the brainstem hemorrhage image.

[0013] Furthermore, the specific steps of the comprehensive analysis to obtain the vascular ultrasonic guided wave monitoring accuracy evaluation value are: obtaining the brainstem hemorrhage image area, the weight factor of the brainstem hemorrhage image quality evaluation value, the weight factor of the brainstem hemorrhage image data evaluation value and the weight factor of the magnetic susceptibility artifact coefficient from the vascular ultrasonic guided wave monitoring database; averaging the brainstem hemorrhage image quality evaluation values ​​of different brainstem hemorrhage image quality detection points and recording them as the first component of the vascular ultrasonic guided wave monitoring accuracy evaluation value; averaging the brainstem hemorrhage image data evaluation values ​​of different brainstem hemorrhage image data detection points and recording them as the second component of the vascular ultrasonic guided wave monitoring accuracy evaluation value; averaging the ratio of the artifact area to the brainstem hemorrhage image area of ​​different vascular ultrasonic guided wave monitoring accuracy detection points and recording them as the third component of the vascular ultrasonic guided wave monitoring accuracy evaluation value; the vascular ultrasonic guided wave monitoring accuracy evaluation value represents the result of quantifying the degree of influence of the first component of the vascular ultrasonic guided wave monitoring accuracy evaluation value, the second component of the vascular ultrasonic guided wave monitoring accuracy evaluation value and the third component of the vascular ultrasonic guided wave monitoring accuracy evaluation value on the vascular ultrasonic guided wave monitoring capability.

[0014] Furthermore, the specific steps of obtaining the vascular ultrasonic guided wave monitoring optimization and adjustment method are: extracting the first judgment value of the brainstem hemorrhage image data evaluation value, the second judgment value of the brainstem hemorrhage image quality evaluation value and the third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value from the database, and comparing and analyzing the brainstem hemorrhage image quality evaluation value, the brainstem hemorrhage image data evaluation value and the vascular ultrasonic guided wave monitoring accuracy evaluation value respectively to obtain the vascular ultrasonic guided wave monitoring accuracy optimization and adjustment method; the vascular ultrasonic guided wave monitoring optimization and adjustment method includes the brainstem hemorrhage image data optimization and adjustment method, the brainstem hemorrhage image quality optimization and adjustment method and the vascular ultrasonic guided wave monitoring accuracy optimization and adjustment method.

[0015] Furthermore, the specific steps of the brainstem hemorrhage image data optimization and adjustment method are: if the brainstem hemorrhage image data evaluation value is greater than the first judgment value of the brainstem hemorrhage image data evaluation value, use the FSE fast imaging sequence to reduce the acquisition time of each sequence and then reduce the phase encoding steps through the SENSE parallel imaging technology; reduce the FOV to increase the pixel density per unit area; use real-time imaging technology such as RT-MRI to achieve real-time image update; if the brainstem hemorrhage image data evaluation value is lower than or equal to the first judgment value of the brainstem hemorrhage image data evaluation value, then there is no need to optimize and adjust the brainstem hemorrhage image data.

[0016] Furthermore, the specific steps of the method for optimizing and adjusting the image quality of brainstem hemorrhage images are as follows: if the image quality assessment value of the brainstem hemorrhage image is greater than or equal to the second judgment value of the image quality assessment value of the brainstem hemorrhage image, then there is no need to optimize and adjust the image quality of the brainstem hemorrhage image; if the image quality assessment value of the brainstem hemorrhage image is lower than the second judgment value of the image quality assessment value of the brainstem hemorrhage image, then the amount of data collection is reduced by using compressed sensing technology, and then the signal penetration depth is improved by using deep radio frequency pulse technology, and more advanced coil technology is used to improve the signal strength and penetration ability.

[0017] Furthermore, the specific steps of the vascular ultrasonic guided wave monitoring accuracy optimization and adjustment method are as follows: if the vascular ultrasonic guided wave monitoring accuracy evaluation value is greater than or equal to the third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value, then there is no need to perform vascular ultrasonic guided wave monitoring accuracy optimization and adjustment; if the vascular ultrasonic guided wave monitoring accuracy evaluation value is lower than the third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value, then a fast imaging sequence is used to shorten the scanning time, reduce the voxel size, apply artifact correction technology, reduce magnetic susceptibility artifacts, and further fine-tune the parameters based on the initial adjustment results, adjust TE, TR or flip angle, re-evaluate the accuracy, compare the threshold, apply noise suppression technology, further reduce the noise level, and use a deep penetration sequence to improve the signal penetration depth.

[0018] An embodiment of the present application provides a method for monitoring vascular ultrasonic guided waves, comprising the following steps: acquiring and processing vascular ultrasonic guided wave monitoring data; analyzing the vascular ultrasonic guided wave monitoring data to obtain a brainstem hemorrhage image data evaluation value and a brainstem hemorrhage image quality evaluation value; comprehensively analyzing the brainstem hemorrhage image quality evaluation value and the brainstem hemorrhage image data evaluation value to obtain a vascular ultrasonic guided wave monitoring accuracy evaluation value; and comparing and analyzing the brainstem hemorrhage image data evaluation value, the brainstem hemorrhage image quality evaluation value, and the vascular ultrasonic guided wave monitoring accuracy evaluation value with a first judgment value of the brainstem hemorrhage image data evaluation value, a second judgment value of the brainstem hemorrhage image quality evaluation value, and a third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value to obtain a vascular ultrasonic guided wave monitoring optimization and adjustment method.

[0019] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0020] 1. By collecting and processing vascular ultrasonic guided wave monitoring data, analyzing the vascular ultrasonic guided wave monitoring data, obtaining brainstem hemorrhage image data evaluation value and brainstem hemorrhage image quality evaluation value, comprehensively analyzing and optimizing and adjusting, the accuracy of the vascular ultrasonic guided wave monitoring system is improved, solving the problem of insufficient accuracy of the vascular ultrasonic guided wave monitoring system in the existing technology.

[0021] 2. By analyzing the vascular ultrasonic guided wave monitoring data, the brainstem hemorrhage imaging data evaluation value and the brainstem hemorrhage image quality evaluation value are obtained. The high-quality images provide high-resolution information, which helps to accurately identify and analyze the details of brainstem hemorrhage, thereby realizing the conversion of subjective image observations into objective data, reducing errors in human judgment, and improving the objectivity of diagnosis.

[0022] 3. Through comprehensive analysis, the accuracy assessment value of vascular ultrasound guided wave monitoring is obtained and optimized and adjusted. The accurate monitoring results provide a reliable basis for multidisciplinary cooperation, promote comprehensive treatment, and thus improve the accuracy of monitoring and diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic structural diagram of a blood vessel ultrasonic guided wave monitoring system provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of an accuracy evaluation function for vascular ultrasound guided wave monitoring provided in an embodiment of the present application;

[0025] Figure 3 A flow chart of a method for monitoring blood vessels using ultrasonic guided waves provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application provide a monitoring system and method for vascular ultrasonic guided waves, thereby solving the problem of insufficient accuracy of vascular ultrasonic guided wave monitoring systems in the prior art. By collecting and processing vascular ultrasonic guided wave monitoring data, analyzing the vascular ultrasonic guided wave monitoring data, obtaining brainstem hemorrhage image data evaluation values ​​and brainstem hemorrhage image quality evaluation values, and comprehensively analyzing and optimizing and adjusting, the accuracy of the vascular ultrasonic guided wave monitoring system is improved.

[0027] The technical solution in the embodiments of the present application is to solve the problem of insufficient accuracy of the above-mentioned vascular ultrasound guided wave monitoring system. The overall idea is as follows:

[0028] By collecting and processing vascular ultrasonic guided wave monitoring data, analyzing the vascular ultrasonic guided wave monitoring data, obtaining brainstem hemorrhage imaging data evaluation value and brainstem hemorrhage imaging image quality evaluation value, comprehensively analyzing and optimizing and adjusting, the accuracy of the vascular ultrasonic guided wave monitoring system is improved.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] like Figure 1As shown, a structural schematic diagram of a vascular ultrasonic guided wave monitoring system provided in an embodiment of the present application, wherein the vascular ultrasonic guided wave monitoring system provided in an embodiment of the present application includes: a vascular ultrasonic guided wave monitoring data acquisition and processing module, a vascular ultrasonic guided wave monitoring data analysis module, a comprehensive analysis module, and an optimization and adjustment module: wherein the vascular ultrasonic guided wave monitoring data acquisition and processing module is used to acquire and process vascular ultrasonic guided wave monitoring data; the vascular ultrasonic guided wave monitoring data analysis module is used to analyze the vascular ultrasonic guided wave monitoring data to obtain a brainstem hemorrhage image data evaluation value and a brainstem hemorrhage image quality evaluation value; the comprehensive analysis module is used to obtain a vascular ultrasonic guided wave monitoring accuracy evaluation value through a comprehensive analysis of the brainstem hemorrhage image quality evaluation value and the brainstem hemorrhage image data evaluation value; the optimization and adjustment module is used to compare and analyze the brainstem hemorrhage image data evaluation value, the brainstem hemorrhage image quality evaluation value, and the vascular ultrasonic guided wave monitoring accuracy evaluation value with a first judgment value of the brainstem hemorrhage image data evaluation value, a second judgment value of the brainstem hemorrhage image quality evaluation value, and a third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value, to obtain a vascular ultrasonic guided wave monitoring optimization and adjustment method.

[0031] Furthermore, the specific steps of collecting and processing vascular ultrasonic guided wave monitoring data are: collecting vascular ultrasonic guided wave monitoring raw data through intravascular ultrasonic diagnostic equipment; cleaning and denoising the vascular ultrasonic guided wave monitoring raw data to obtain vascular ultrasonic guided wave monitoring data; the vascular ultrasonic guided wave monitoring data includes brainstem hemorrhage image data and brainstem hemorrhage image quality data.

[0032] Furthermore, the specific steps of obtaining the brainstem hemorrhage image data evaluation value are as follows: the brainstem hemorrhage image data includes brainstem hemorrhage image voxels, scanning time, and brainstem hemorrhage image imaging speed; obtaining a brainstem hemorrhage image voxel threshold, a scanning time threshold, a brainstem hemorrhage image imaging speed standard value, a brainstem hemorrhage image voxel weight factor, a scanning time weight factor, and a brainstem hemorrhage image imaging speed weight factor from a vascular ultrasound guided wave monitoring database; recording the ratio of the brainstem hemorrhage image voxels at the brainstem hemorrhage image data detection point to the brainstem hemorrhage image voxel threshold as the first component of the brainstem hemorrhage image data evaluation value; The ratio of the scanning time of different brainstem hemorrhage imaging data detection segments to the scanning time threshold is averaged and recorded as the second component of the brainstem hemorrhage imaging data evaluation value; the standard value of the brainstem hemorrhage image imaging speed and the degree to which the brainstem hemorrhage image imaging speed deviates from the standard value are processed by ratio, and the result obtained by the ratio processing is recorded as the third component of the brainstem hemorrhage image data evaluation value; the brainstem hemorrhage image data evaluation value represents the result of quantifying the brainstem hemorrhage related information by the first component of the brainstem hemorrhage image data evaluation value, the second component of the brainstem hemorrhage image data evaluation value and the third component of the brainstem hemorrhage image data evaluation value.

[0033] In this embodiment, the specific method for analyzing and obtaining the brainstem hemorrhage image data evaluation value is as follows:

[0034]

[0035] μ1+μ2+μ3=1;

[0036] The preset brainstem hemorrhage image data detection points are numbered in sequence, S0 represents the number of the brainstem hemorrhage image data detection point under the T0-th brainstem hemorrhage image data detection segment, S0=1, 2, ..., S, where S represents the total number of brainstem hemorrhage image data detection points.

[0037] The preset brainstem hemorrhage image data time is divided into brainstem hemorrhage image data detection segments of the same time length, T0 represents the number of the brainstem hemorrhage image data detection segment, T0=1,2,...,T, T represents the total number of brainstem hemorrhage image data detection segments.

[0038] It represents the brainstem hemorrhage image data evaluation value of the S0th brainstem hemorrhage image data detection point.

[0039] It represents the brainstem hemorrhage image voxel at the S0th brainstem hemorrhage image data detection point.

[0040] X1 represents the brainstem hemorrhage image voxel threshold, which is a preset brainstem hemorrhage image voxel threshold obtained from the vascular ultrasound guided wave monitoring database, and can be the average value of the brainstem hemorrhage image voxel at the detection point of the historical brainstem hemorrhage imaging data in the vascular ultrasound guided wave monitoring database.

[0041] Indicates the scanning time of the T0th brainstem hemorrhage image data detection segment.

[0042] A1 represents a scan time threshold, which is a preset scan time threshold obtained from a vascular ultrasound guided wave monitoring database, and may be an average scan time value under a detection segment of historical brainstem hemorrhage imaging data from the vascular ultrasound guided wave monitoring database.

[0043] V1 represents the standard value of the imaging velocity of the brainstem hemorrhage image, which is a preset brainstem hemorrhage imaging velocity threshold obtained from the vascular ultrasonic guided wave monitoring database, and can be the average value of the brainstem hemorrhage image imaging velocity at the detection point of the historical brainstem hemorrhage imaging data from the vascular ultrasonic guided wave monitoring database.

[0044] It represents the imaging speed of the brainstem hemorrhage image at the S0th brainstem hemorrhage image data detection point.

[0045] μ1 is the preset brainstem hemorrhage image voxel weight factor obtained from the vascular ultrasound guided wave monitoring database.

[0046] μ2 is the preset scan time weight factor obtained from the vascular ultrasound guided wave monitoring database.

[0047] μ3 is the preset brainstem hemorrhage imaging velocity weight factor obtained from the vascular ultrasound guided wave monitoring database.

[0048] By obtaining a mapping table of weight factors from a database, the corresponding weight factors can be quickly extracted based on the current brainstem hemorrhage image voxel, scan time, and brainstem hemorrhage image imaging speed. For example, the weight factor for brainstem hemorrhage image voxels, the weight factor for scan time, and the weight factor for brainstem hemorrhage image imaging speed are derived. This mapping table defines a clear set of association rules that converts the specific values ​​of brainstem hemorrhage image voxels, scan time, and brainstem hemorrhage image imaging speed into their corresponding weight factors. Under this mechanism, whether achieving a one-to-one exact match or a many-to-one relationship where multiple parameters are aggregated into a single weight, the dynamic acquisition of weight factors can be effectively achieved.

[0049] The larger the voxel size of the brainstem hemorrhage image, the more data points need to be collected to maintain the image resolution, which usually prolongs the scanning time. The faster the imaging speed of the brainstem hemorrhage image, the shorter the time required to complete the scan of the same range. The shorter the scanning time. The smaller the voxel size of the brainstem hemorrhage image, the longer the data collection time for each voxel may be, resulting in slower imaging speed. The slower the imaging speed of the brainstem hemorrhage image.

[0050] There is a positive correlation between the voxels of brainstem hemorrhage images and the evaluation value of brainstem hemorrhage image data. The larger the voxels of brainstem hemorrhage images, the blurrier the image and the loss of details. The larger the voxels of brainstem hemorrhage images, the greater the evaluation value of brainstem hemorrhage image data; there is a positive correlation between scanning time and the evaluation value of brainstem hemorrhage image data, which will increase the risk of patient movement or discomfort, resulting in image artifacts. The longer the scanning time, the greater the evaluation value of brainstem hemorrhage image data; there is a positive correlation between the absolute value of the difference between the imaging speed of brainstem hemorrhage images and the standard value of the imaging speed of brainstem hemorrhage images and the evaluation value of brainstem hemorrhage image data, which will lead to insufficient data acquisition and deterioration of image quality. The greater the absolute value of the difference between the imaging speed of brainstem hemorrhage images and the standard value of the imaging speed of brainstem hemorrhage images, the greater the evaluation value of brainstem hemorrhage image data.

[0051] Furthermore, the specific process of obtaining the image quality evaluation value of brainstem hemorrhage image is as follows: the image quality data of brainstem hemorrhage image includes brainstem hemorrhage image voxels, average pixel intensity of the brainstem hemorrhage image surrounding tissue area, average pixel intensity of the brainstem hemorrhage image surrounding tissue area and magnetic resonance signal intensity; the voxel threshold of brainstem hemorrhage image, the standard value of noise in the brainstem hemorrhage image vascular area, the standard value of noise in the brainstem hemorrhage image surrounding tissue area, magnetic resonance signal intensity threshold, weight factor of contrast-to-noise ratio coefficient, weight factor of brainstem hemorrhage image voxels and weight factor of magnetic resonance signal intensity are obtained from the vascular ultrasonic guided wave monitoring database; the average pixel intensity of the brainstem hemorrhage image surrounding tissue area and the brainstem hemorrhage image surrounding tissue area are combined. The degree to which the average pixel intensity of the surrounding tissue area deviates from the standard value is processed as a proportion and recorded as the contrast-to-noise ratio coefficient; the ratio of the brainstem hemorrhage image voxel threshold and the brainstem hemorrhage image voxel at the brainstem hemorrhage image quality detection point is recorded as the second component of the brainstem hemorrhage image quality assessment value; the ratio of the magnetic resonance signal intensity at the brainstem hemorrhage image quality detection point to the magnetic resonance signal intensity threshold is recorded as the third component of the brainstem hemorrhage image quality assessment value; the brainstem hemorrhage image quality assessment value represents the effect of the contrast-to-noise ratio coefficient, the second component of the brainstem hemorrhage image quality assessment value, and the third component of the brainstem hemorrhage image quality assessment value on quantifying the image quality of the brainstem hemorrhage image.

[0052] In this embodiment, the specific method for obtaining the image quality assessment value of the brainstem hemorrhage image by analysis is as follows:

[0053]

[0054] σ1+σ2+σ3=1;

[0055] The preset brainstem hemorrhage image quality detection points are numbered in sequence, N0 represents the number of the brainstem hemorrhage image quality detection point, N0=1, 2, ..., N, and N represents the total number of the brainstem hemorrhage image quality detection points.

[0056] The preset brainstem hemorrhage imaging depth detection points are numbered in sequence, M0 represents the number of the brainstem hemorrhage imaging depth detection point, M0=1, 2, ..., M, and M represents the total number of the brainstem hemorrhage imaging depth detection points.

[0057] Indicates the image quality assessment value of the brainstem hemorrhage image at the N0th brainstem hemorrhage image quality detection point.

[0058] It represents the contrast-to-noise ratio coefficient at the N0th brainstem hemorrhage image quality detection point.

[0059] H1 represents the brainstem hemorrhage image voxel threshold, which is a preset brainstem hemorrhage image voxel threshold obtained from the vascular ultrasound guided wave monitoring database, and can be the average value of the brainstem hemorrhage image voxels at the image quality detection point of historical brainstem hemorrhage images in the vascular ultrasound guided wave monitoring database.

[0060] Represents the voxel of the brainstem hemorrhage image at the N0th brainstem hemorrhage image quality detection point.

[0061] It represents the average pixel intensity of the tissue area surrounding the brainstem hemorrhage image at the N0th brainstem hemorrhage image quality detection point. Magnetic resonance imaging is used to distinguish hemorrhage, edema, and normal tissue. The signal difference between blood vessels and surrounding tissue is utilized to extract the signal intensity value pixel by pixel in the vascular area, and the intensity of all pixels in the vascular area is averaged.

[0062] It represents the average pixel intensity of the tissue area surrounding the brainstem hemorrhage image at the N0th brainstem hemorrhage image quality detection point. Magnetic resonance imaging is sensitive to edema and can clearly show the high-signal area around the hemorrhage. The hemorrhage mask is morphologically expanded to form a spherical core with a radius of 5 mm. The expanded area is subtracted from the original hemorrhage area to obtain the surrounding tissue mask. Using medical imaging software, the surrounding tissue mask is superimposed on the original image, and the intensity values ​​of all pixels in the mask are extracted.

[0063] Z Q It represents the standard value of noise in the blood vessel area of ​​brainstem hemorrhage images, which is the preset standard value of noise in the blood vessel area of ​​brainstem hemorrhage images obtained from the vascular ultrasound guided wave monitoring database.

[0064] Z P It represents the noise standard value of the tissue area around the brainstem hemorrhage image, which is the preset noise standard value of the tissue area around the brainstem hemorrhage image obtained from the vascular ultrasound guided wave monitoring database.

[0065] It represents the magnetic resonance signal intensity at the imaging depth detection point of the M0th brainstem hemorrhage image.

[0066] η1 represents the magnetic resonance signal intensity threshold, which is a preset magnetic resonance signal intensity threshold obtained from the vascular ultrasonic guided wave monitoring database, and can be the magnetic resonance signal intensity at the imaging depth detection point of the historical brainstem hemorrhage image obtained from the vascular ultrasonic guided wave monitoring database.

[0067] σ1 is the preset contrast-to-noise ratio coefficient weighting factor obtained from the vascular ultrasound guided wave monitoring database.

[0068] σ2 is the preset brainstem hemorrhage image voxel weight factor obtained from the vascular ultrasound guided wave monitoring database.

[0069] σ3 is the preset magnetic resonance signal intensity weight factor obtained from the vascular ultrasound guided wave monitoring database.

[0070] By obtaining a mapping table of weight factors from a database, the corresponding weight factors can be quickly extracted based on the contrast-to-noise ratio coefficient, brainstem hemorrhage image voxels, and MRI signal intensity. For example, the weight factor for the contrast-to-noise ratio coefficient, the weight factor for the brainstem hemorrhage image voxels, and the weight factor for the MRI signal intensity. This mapping table defines a clear set of association rules that convert the contrast-to-noise ratio coefficient, brainstem hemorrhage image voxels, and MRI signal intensity into their corresponding weight factors. Under this mechanism, whether achieving a one-to-one exact match or a many-to-one relationship where multiple parameters are aggregated into a single weight, the dynamic acquisition of weight factors can be effectively achieved.

[0071] The voxels of brainstem hemorrhage images affect the resolution of the image, which in turn affects the accurate measurement of signal intensity. The larger the voxels of the brainstem hemorrhage image, the greater the average pixel intensity of the tissue area surrounding the brainstem hemorrhage image and the average pixel intensity of the tissue area surrounding the brainstem hemorrhage image; there is usually a difference between the average pixel intensity of the tissue area surrounding the brainstem hemorrhage image and the average pixel intensity of the tissue area surrounding the brainstem hemorrhage image. This difference helps to distinguish these two types of areas in the image. The vascular area usually contains more blood and therefore may show a higher signal intensity in the magnetic resonance image.

[0072] There is a negative correlation between the voxel number of brainstem hemorrhage images and the image quality assessment value of brainstem hemorrhage images. The larger the voxel number, the lower the image resolution and the worse the detail performance. The larger the voxel number of brainstem hemorrhage images, the smaller the image quality assessment value of brainstem hemorrhage images; there is a positive correlation between the contrast-to-noise ratio coefficient and the image quality assessment value of brainstem hemorrhage images. Appropriate improvement of the average intensity of the vascular area can increase the contrast between the blood vessels and surrounding tissues and make the vascular structure clearer. The larger the contrast-to-noise ratio coefficient, the greater the image quality assessment value of the brainstem hemorrhage images; there is a negative correlation between the ratio of magnetic resonance signal intensity to magnetic resonance signal intensity threshold and the image quality assessment value of brainstem hemorrhage images. Excessive signal intensity may lead to image saturation, while too low signal intensity may lead to insufficient image information. The larger the ratio of magnetic resonance signal intensity to magnetic resonance signal intensity threshold, the smaller the image quality assessment value of brainstem hemorrhage images.

[0073] Furthermore, the specific steps for comprehensively analyzing and obtaining the accuracy evaluation value of vascular ultrasonic guided wave monitoring are as follows: obtaining the brainstem hemorrhage image area, the weight factor of the brainstem hemorrhage image quality evaluation value, the weight factor of the brainstem hemorrhage image data evaluation value, and the weight factor of the magnetic susceptibility artifact coefficient from the vascular ultrasonic guided wave monitoring database; averaging the brainstem hemorrhage image quality evaluation values ​​of different brainstem hemorrhage image quality detection points and recording them as the first component of the vascular ultrasonic guided wave monitoring accuracy evaluation value; averaging the brainstem hemorrhage image data evaluation values ​​of different brainstem hemorrhage image data detection points and recording them as the second component of the vascular ultrasonic guided wave monitoring accuracy evaluation value; averaging the ratio of the artifact area to the brainstem hemorrhage image area of ​​different vascular ultrasonic guided wave monitoring accuracy detection points and recording them as the third component of the vascular ultrasonic guided wave monitoring accuracy evaluation value; the vascular ultrasonic guided wave monitoring accuracy evaluation value represents the result of quantifying the degree of influence of the first component of the vascular ultrasonic guided wave monitoring accuracy evaluation value, the second component of the vascular ultrasonic guided wave monitoring accuracy evaluation value, and the third component of the vascular ultrasonic guided wave monitoring accuracy evaluation value on the vascular ultrasonic guided wave monitoring capability.

[0074] In this embodiment, the specific method for obtaining the accuracy evaluation value of vascular ultrasound guided wave monitoring is as follows:

[0075]

[0076] ρ1+ρ2+ρ3=1;

[0077] The preset vascular ultrasound guided wave monitoring accuracy detection points are numbered in sequence, K0 represents the number of the vascular ultrasound guided wave monitoring accuracy detection point, K0 = 1, 2, ..., K, K represents the total number of vascular ultrasound guided wave monitoring accuracy detection points.

[0078] ξ represents the accuracy evaluation value of vascular ultrasound guided wave monitoring.

[0079] Indicates the image quality assessment value of the brainstem hemorrhage image at the N0th brainstem hemorrhage image quality detection point.

[0080] It represents the brainstem hemorrhage image data evaluation value of the S0th brainstem hemorrhage image data detection point.

[0081] It represents the magnetic susceptibility artifact coefficient at the K0th vascular ultrasound guided wave monitoring accuracy detection point.

[0082] It represents the area of ​​the artifact region under the K0th vascular ultrasound guided wave monitoring accuracy detection point.

[0083] ω represents the image area of ​​brainstem hemorrhage.

[0084] ρ1 is the weight factor of the preset brainstem hemorrhage image data evaluation value obtained from the vascular ultrasound guided wave monitoring database.

[0085] ρ2 is the weight factor of the preset brainstem hemorrhage image data evaluation value obtained from the vascular ultrasound guided wave monitoring database.

[0086] ρ3 is the preset magnetic susceptibility artifact coefficient weight factor obtained from the vascular ultrasound guided wave monitoring database.

[0087] By obtaining a mapping table of weight factors from a database, corresponding weight factors can be quickly extracted based on the brainstem hemorrhage image quality assessment value, brainstem hemorrhage image data assessment value, and magnetic susceptibility artifact coefficient. For example, the weight factor for the brainstem hemorrhage image quality assessment value, the weight factor for the brainstem hemorrhage image data assessment value, and the weight factor for the magnetic susceptibility artifact coefficient are derived. This mapping table defines a clear set of association rules that convert the brainstem hemorrhage image quality assessment value, the brainstem hemorrhage image data assessment value, and the magnetic susceptibility artifact coefficient into their corresponding weight factors. This mechanism effectively achieves dynamic acquisition of weight factors, whether achieving a one-to-one exact match or a many-to-one relationship where multiple parameters are aggregated into a single weight.

[0088] Table 1 is an example table of accuracy evaluation values ​​for vascular ultrasound guided wave monitoring. The example parameters in Table 1 are only taken from one vascular ultrasound guided wave monitoring accuracy detection point for illustration. The weight factor ρ1 is set to 0.5, the weight factor ρ2 is set to 0.2, the weight factor ρ3 is set to 0.3, and the area of ​​the brainstem hemorrhage image is 4. Table 1 is shown below.

[0089] Table 1 Example of accuracy evaluation values ​​for vascular ultrasound guided wave monitoring

[0090]

[0091] The larger the image quality assessment value of brainstem hemorrhage imaging, the better the image quality of brainstem hemorrhage imaging, the more reliable the brainstem hemorrhage imaging data, and the smaller the brainstem hemorrhage imaging data assessment value; the area of ​​the artifact region refers to the non-real image information caused by technical or patient reasons in the image. The larger the area of ​​the artifact region, the more it will interfere with the clarity of the image, and the smaller the image quality assessment value of the brainstem hemorrhage imaging; the larger the area of ​​the artifact region, the more it will affect the accuracy of the brainstem hemorrhage imaging data assessment value, because the artifact will be mistaken for the hemorrhage area or cover up the real hemorrhage area, and the larger the brainstem hemorrhage imaging data assessment value; the larger the image quality assessment value of the brainstem hemorrhage imaging, the higher the image quality of the brainstem hemorrhage imaging, which helps to more accurately measure the hemorrhage image area.

[0092] Table 1 shows that there is a positive correlation between the image quality assessment value of brainstem hemorrhage images and the accuracy assessment value of vascular ultrasound guided wave monitoring. A larger image quality assessment value indicates better image clarity and accuracy, which helps to more accurately identify and locate the bleeding area during vascular ultrasound guided wave monitoring. A larger image quality assessment value for brainstem hemorrhage images is associated with a higher accuracy assessment value for vascular ultrasound guided wave monitoring. A negative correlation exists between the image data assessment value of brainstem hemorrhage and the accuracy assessment value for vascular ultrasound guided wave monitoring. A larger image data assessment value indicates lower data accuracy, making it difficult to accurately determine the bleeding condition. A larger image data assessment value indicates a lower accuracy assessment value for vascular ultrasound guided wave monitoring. A negative correlation exists between the ratio of the artifact area to the brainstem hemorrhage image area and the accuracy assessment value for vascular ultrasound guided wave monitoring. More interference information in the image can mislead vascular ultrasound guided wave monitoring. A larger ratio of the artifact area to the brainstem hemorrhage image area indicates a lower accuracy assessment value for vascular ultrasound guided wave monitoring.

[0093] Depend on Figure 2 As shown, this is a schematic diagram of the accuracy evaluation value function of vascular ultrasonic guided wave monitoring provided in an embodiment of the present application; x is the positive half axis of the horizontal coordinate, y is the positive half axis of the vertical coordinate, the weight factor ρ1 is set to 0.5, the weight factor ρ2 is set to 0.2, the weight factor ρ3 is set to 0.3, and the area of ​​the brainstem hemorrhage image is 4.

[0094] Curve a indicates that if the brainstem hemorrhage image data evaluation value is set to a fixed value of 1, the artifact area is set to a fixed value of 1, the brainstem hemorrhage image quality evaluation value is x, and the vascular ultrasound guided wave monitoring accuracy evaluation value is y, the vascular ultrasound guided wave monitoring accuracy evaluation value increases with the increase of the brainstem hemorrhage image quality evaluation value.

[0095] Furthermore, the specific steps of obtaining the vascular ultrasonic guided wave monitoring optimization and adjustment method are: extracting the first judgment value of the brainstem hemorrhage image data evaluation value, the second judgment value of the brainstem hemorrhage image quality evaluation value and the third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value from the database, and comparing and analyzing the brainstem hemorrhage image quality evaluation value, the brainstem hemorrhage image data evaluation value and the vascular ultrasonic guided wave monitoring accuracy evaluation value respectively, to obtain the vascular ultrasonic guided wave monitoring accuracy optimization and adjustment method; the vascular ultrasonic guided wave monitoring optimization and adjustment method includes the brainstem hemorrhage image data optimization and adjustment method, the brainstem hemorrhage image quality optimization and adjustment method and the vascular ultrasonic guided wave monitoring accuracy optimization and adjustment method.

[0096] Furthermore, the specific steps of the brainstem hemorrhage image data optimization and adjustment method are as follows: if the brainstem hemorrhage image data evaluation value is greater than the first judgment value of the brainstem hemorrhage image data evaluation value, use the FSE fast imaging sequence to reduce the acquisition time of each sequence and then use the SENSE parallel imaging technology to reduce the phase encoding steps; reduce the FOV to increase the pixel density per unit area; use real-time imaging technology such as RT-MRI to achieve real-time image updates; if the brainstem hemorrhage image data evaluation value is lower than or equal to the first judgment value of the brainstem hemorrhage image data evaluation value, then there is no need to optimize and adjust the brainstem hemorrhage image data.

[0097] In this embodiment, FSE is a fast imaging sequence that speeds up imaging by reducing the acquisition time of each echo and using multiple echoes for filling, thereby obtaining complete image data in a shorter time. SENSE is a parallel imaging technology that uses multiple receiving coils to simultaneously acquire data and reconstructs a complete image through an algorithm. This method can reduce the number of phase encoding steps and further shorten the imaging time. On the basis of using the FSE sequence, further application of SENSE technology can further speed up the imaging speed while maintaining image quality. FOV is the range of the imaging area. Reducing the FOV means focusing on a smaller area, thereby increasing the pixel density in the area and making the image clearer. In the case of brainstem hemorrhage, the focus is usually on the hemorrhage area. RT-MRI is a real-time imaging technology that can update images in almost real time and display dynamic changes. This technology is very useful for monitoring the dynamic process of brainstem hemorrhage. On the basis of quickly acquiring static images, real-time imaging technology can provide dynamic information to help doctors monitor changes in bleeding conditions in real time and adjust treatment plans in a timely manner.

[0098] Furthermore, the specific steps of the method for optimizing and adjusting the image quality of brainstem hemorrhage images are as follows: if the image quality assessment value of the brainstem hemorrhage image is greater than or equal to the second judgment value of the image quality assessment value of the brainstem hemorrhage image, then there is no need to optimize and adjust the image quality of the brainstem hemorrhage image; if the image quality assessment value of the brainstem hemorrhage image is lower than the second judgment value of the image quality assessment value of the brainstem hemorrhage image, then the amount of data collection is reduced by using compressed sensing technology, and then the signal penetration depth is improved by using deep radio frequency pulse technology, and more advanced coil technology is used to improve the signal strength and penetration ability.

[0099] In this embodiment, the image quality assessment value of the brainstem hemorrhage image is lower than the second judgment value, indicating that the current image quality is insufficient to meet diagnostic requirements. Compressed sensing technology is used to reduce the amount of data collected. This step aims to improve data acquisition efficiency while ensuring basic image quality, creating conditions for subsequent signal optimization. Deep radiofrequency pulse technology is used to improve signal penetration depth. While reducing data acquisition volume, this further optimizes signal penetration depth and ensures clear imaging of deep brainstem tissue. More advanced coil technology is used to increase signal strength and penetration. This step, based on the previous two steps, further enhances signal strength and penetration through hardware technology improvements, ultimately achieving the goal of improving image quality.

[0100] Furthermore, the specific steps of the method for optimizing and adjusting the accuracy of vascular ultrasonic guided wave monitoring are as follows: if the accuracy evaluation value of vascular ultrasonic guided wave monitoring is greater than or equal to the third judgment value of the accuracy evaluation value of vascular ultrasonic guided wave monitoring, then there is no need to optimize and adjust the accuracy of vascular ultrasonic guided wave monitoring; if the accuracy evaluation value of vascular ultrasonic guided wave monitoring is lower than the third judgment value of the accuracy evaluation value of vascular ultrasonic guided wave monitoring, then a fast imaging sequence is used to shorten the scanning time, reduce the voxel size, apply artifact correction technology, reduce magnetic susceptibility artifacts, and further fine-tune the parameters based on the initial adjustment results, adjust TE, TR or flip angle, re-evaluate the accuracy, compare the threshold, apply noise suppression technology, further reduce the noise level, and use a deep penetration sequence to improve the signal penetration depth.

[0101] In this embodiment, fast imaging sequences (such as FSE, Turbo Magnetic susceptibility artifacts are caused by differences in magnetic susceptibility between tissues. Specific correction techniques can reduce these artifacts, improve image quality, and minimize diagnostic interference. Based on preliminary imaging results, parameters such as echo time (TE), repetition time (TR), or flip angle are adjusted to optimize image contrast and signal-to-noise ratio (SNR). Fine-tune the image to better meet diagnostic requirements. After parameter adjustment, image accuracy is reassessed and compared against preset thresholds to ensure diagnostic criteria are met. Noise suppression algorithms or techniques are used to reduce random noise in the image, improve the SNR, enhance image clarity, and minimize diagnostic interference. Deep-penetration sequences are used to improve signal penetration, such as deep radiofrequency pulse technology, to enhance signal penetration into deep tissues, ensuring clear imaging of deep vascular structures.

[0102] like Figure 3 As shown, a flow chart of a method for monitoring vascular ultrasonic guided waves provided in an embodiment of the present application, a method for monitoring vascular ultrasonic guided waves provided in an embodiment of the present application includes: collecting and processing vascular ultrasonic guided wave monitoring data; analyzing the vascular ultrasonic guided wave monitoring data to obtain a brainstem hemorrhage image data evaluation value and a brainstem hemorrhage image quality evaluation value; comprehensively analyzing the brainstem hemorrhage image quality evaluation value and the brainstem hemorrhage image data evaluation value to obtain a vascular ultrasonic guided wave monitoring accuracy evaluation value; comparing and analyzing the brainstem hemorrhage image data evaluation value, the brainstem hemorrhage image quality evaluation value and the vascular ultrasonic guided wave monitoring accuracy evaluation value with a first judgment value of the brainstem hemorrhage image data evaluation value, a second judgment value of the brainstem hemorrhage image quality evaluation value and a third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value to obtain a vascular ultrasonic guided wave monitoring optimization and adjustment method.

[0103] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0107] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0108] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

Claims

1. A monitoring system for blood vessel ultrasonic guided waves, characterized in that: include: Vascular ultrasonic guided wave monitoring data acquisition and processing module, vascular ultrasonic guided wave monitoring data analysis module, comprehensive analysis module and optimization and adjustment module: Wherein, the vascular ultrasonic guided wave monitoring data acquisition and processing module is used to acquire and process vascular ultrasonic guided wave monitoring data; The vascular ultrasonic guided wave monitoring data analysis module is used to analyze the vascular ultrasonic guided wave monitoring data to obtain a brainstem hemorrhage image data evaluation value and a brainstem hemorrhage image quality evaluation value; The comprehensive analysis module is used to obtain the accuracy evaluation value of vascular ultrasound guided wave monitoring by comprehensive analysis of the brainstem hemorrhage image quality evaluation value and the brainstem hemorrhage image data evaluation value; The optimization and adjustment module is used to compare and analyze the brainstem hemorrhage image data evaluation value, the brainstem hemorrhage image quality evaluation value and the vascular ultrasonic guided wave monitoring accuracy evaluation value with the first judgment value of the brainstem hemorrhage image data evaluation value, the second judgment value of the brainstem hemorrhage image quality evaluation value and the third judgment value of the vascular ultrasonic guided wave monitoring accuracy evaluation value to obtain a vascular ultrasonic guided wave monitoring optimization and adjustment method.

2. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 1, characterized in that: The specific steps of collecting and processing vascular ultrasound guided wave monitoring data are as follows: Collecting raw data of vascular ultrasound guided wave monitoring through intravascular ultrasound diagnostic equipment; Cleaning and denoising the raw data of vascular ultrasonic guided wave monitoring to obtain vascular ultrasonic guided wave monitoring data; The vascular ultrasonic guided wave monitoring data includes brainstem hemorrhage image data and brainstem hemorrhage image quality data.

3. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 1, characterized in that: The specific steps of obtaining the brainstem hemorrhage image data evaluation value are: The brainstem hemorrhage image data includes brainstem hemorrhage image voxels, scanning time, and brainstem hemorrhage image imaging speed; Obtaining a brainstem hemorrhage image voxel threshold, a scan time threshold, a brainstem hemorrhage image imaging speed standard value, a brainstem hemorrhage image voxel weight factor, a scan time weight factor, and a brainstem hemorrhage image imaging speed weight factor from a vascular ultrasound guided wave monitoring database; The ratio of the brainstem hemorrhage image voxel at the brainstem hemorrhage image data detection point to the brainstem hemorrhage image voxel threshold is recorded as the first component of the brainstem hemorrhage image data evaluation value; The ratio of the scanning time of different brainstem hemorrhage imaging data detection segments to the scanning time threshold is averaged and recorded as the second component of the brainstem hemorrhage imaging data evaluation value; The standard value of the imaging velocity of the brainstem hemorrhage image and the degree to which the imaging velocity of the brainstem hemorrhage image deviates from the standard value are processed by proportion, and the result obtained by the proportion processing is recorded as the third component of the brainstem hemorrhage image data evaluation value; The brainstem hemorrhage image data evaluation value represents the result of quantifying the brainstem hemorrhage related information by the first component of the brainstem hemorrhage image data evaluation value, the second component of the brainstem hemorrhage image data evaluation value, and the third component of the brainstem hemorrhage image data evaluation value.

4. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 1, characterized in that: The specific process of obtaining the image quality assessment value of brainstem hemorrhage is as follows: The brainstem hemorrhage image quality data includes brainstem hemorrhage image voxels, average pixel intensity of the tissue area surrounding the brainstem hemorrhage image, average pixel intensity of the tissue area surrounding the brainstem hemorrhage image and magnetic resonance signal intensity; The brainstem hemorrhage image voxel threshold, the brainstem hemorrhage image vascular region noise standard value, the brainstem hemorrhage image surrounding tissue region noise standard value, the magnetic resonance signal intensity threshold, the contrast-to-noise ratio coefficient weight factor, the brainstem hemorrhage image voxel weight factor, and the magnetic resonance signal intensity weight factor are obtained from the vascular ultrasound guided wave monitoring database; The average pixel intensity of the tissue area around the brainstem hemorrhage image and the degree to which the average pixel intensity of the tissue area around the brainstem hemorrhage image deviates from the standard value are processed as a ratio, which is recorded as the contrast-to-noise ratio coefficient; The result of the brainstem hemorrhage image voxel threshold and the ratio of the brainstem hemorrhage image voxels at the brainstem hemorrhage image quality detection point is recorded as the second component of the brainstem hemorrhage image quality assessment value; The ratio of the magnetic resonance signal intensity of the brainstem hemorrhage image quality detection point to the magnetic resonance signal intensity threshold is recorded as the third component of the brainstem hemorrhage image quality assessment value; The brainstem hemorrhage image quality assessment value represents the effect of the contrast-to-noise ratio coefficient, the second component of the brainstem hemorrhage image quality assessment value, and the third component of the brainstem hemorrhage image quality assessment value jointly quantifying the image quality of the brainstem hemorrhage image.

5. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 1, characterized in that: The specific steps of comprehensively analyzing and obtaining the accuracy evaluation value of vascular ultrasound guided wave monitoring are as follows: The brainstem hemorrhage image area, the weight factor of the brainstem hemorrhage image quality assessment value, the weight factor of the brainstem hemorrhage image data assessment value, and the weight factor of the magnetic susceptibility artifact coefficient are obtained from the vascular ultrasound guided wave monitoring database; The image quality assessment values ​​of brainstem hemorrhage at different brainstem hemorrhage image quality detection points are averaged and recorded as the first component of the accuracy assessment value of vascular ultrasound guided wave monitoring; The brainstem hemorrhage image data evaluation values ​​of different brainstem hemorrhage image data detection points are averaged and recorded as the second component of the vascular ultrasound guided wave monitoring accuracy evaluation value; The ratio of the artifact area and the brainstem hemorrhage image area at different vascular ultrasound guided wave monitoring accuracy detection points is averaged and recorded as the third component of the vascular ultrasound guided wave monitoring accuracy assessment value; The vascular ultrasonic guided wave monitoring accuracy assessment value represents the result of quantifying the degree of influence of the vascular ultrasonic guided wave monitoring accuracy assessment value first component, the vascular ultrasonic guided wave monitoring accuracy assessment value second component and the vascular ultrasonic guided wave monitoring accuracy assessment value third component on the vascular ultrasonic guided wave monitoring capability.

6. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 1, characterized in that: The specific steps of obtaining the optimization and adjustment method for vascular ultrasound guided wave monitoring are: The first judgment value of the brainstem hemorrhage image data evaluation value, the second judgment value of the brainstem hemorrhage image quality evaluation value, and the third judgment value of the vascular ultrasound guided wave monitoring accuracy evaluation value are extracted from the database, and compared with the brainstem hemorrhage image quality evaluation value, the brainstem hemorrhage image data evaluation value, and the vascular ultrasound guided wave monitoring accuracy evaluation value, respectively, to obtain an optimization adjustment method for vascular ultrasound guided wave monitoring accuracy; The vascular ultrasonic guided wave monitoring optimization and adjustment method includes a brainstem hemorrhage image data optimization and adjustment method, a brainstem hemorrhage image quality optimization and adjustment method, and a vascular ultrasonic guided wave monitoring accuracy optimization and adjustment method.

7. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 6, characterized in that: The specific steps of the brainstem hemorrhage image data optimization and adjustment method are as follows: If the brainstem hemorrhage imaging data assessment value is greater than the first judgment value of the brainstem hemorrhage imaging data assessment value, use the FSE fast imaging sequence to reduce the acquisition time of each sequence and then use the SENSE parallel imaging technology to reduce the number of phase encoding steps; reduce the FOV to increase the pixel density per unit area; use real-time imaging technology such as RT-MRI to achieve real-time image updates; If the brainstem hemorrhage image data evaluation value is lower than or equal to the first judgment value of the brainstem hemorrhage image data evaluation value, there is no need to optimize and adjust the brainstem hemorrhage image data.

8. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 6, characterized in that: The specific steps of the method for optimizing and adjusting the image quality of brainstem hemorrhage images are as follows: If the image quality assessment value of the brainstem hemorrhage image is greater than or equal to the second judgment value of the image quality assessment value of the brainstem hemorrhage image, then there is no need to perform optimization adjustment of the image quality of the brainstem hemorrhage image; If the image quality assessment value of the brainstem hemorrhage image is lower than the second judgment value of the brainstem hemorrhage image quality assessment value, the data collection amount is reduced by using compressed sensing technology, and the signal penetration depth is improved by using deep radio frequency pulse technology, and more advanced coil technology is used to improve the signal strength and penetration ability.

9. A blood vessel ultrasonic guided wave monitoring system as claimed in claim 6, characterized in that: The specific steps of the method for optimizing and adjusting the accuracy of vascular ultrasound guided wave monitoring are as follows: If the vascular ultrasonic guided wave monitoring accuracy evaluation value is greater than or equal to the vascular ultrasonic guided wave monitoring accuracy evaluation value third determination value, then there is no need to perform vascular ultrasonic guided wave monitoring accuracy optimization adjustment; If the accuracy assessment value of vascular ultrasound guided wave monitoring is lower than the third judgment value of the accuracy assessment value of vascular ultrasound guided wave monitoring, a fast imaging sequence is used to shorten the scanning time, reduce the voxel size, apply artifact correction technology, reduce magnetic susceptibility artifacts, and further fine-tune the parameters based on the initial adjustment results, adjust TE, TR or flip angle, re-evaluate the accuracy, compare thresholds, apply noise suppression technology to further reduce the noise level, and use a deep penetration sequence to improve the signal penetration depth.

10. A method for monitoring blood vessels using ultrasonic guided waves, characterized in that: The following steps are involved: Collect and process vascular ultrasound guided wave monitoring data; Analyze the vascular ultrasound guided wave monitoring data to obtain the brainstem hemorrhage imaging data evaluation value and the brainstem hemorrhage imaging image quality evaluation value; The accuracy assessment value of vascular ultrasound guided wave monitoring was obtained by comprehensive analysis of the image quality assessment value and the data assessment value of brainstem hemorrhage imaging. The brainstem hemorrhage imaging data evaluation value, brainstem hemorrhage imaging image quality evaluation value and vascular ultrasound guided wave monitoring accuracy evaluation value were compared and analyzed with the first judgment value of the brainstem hemorrhage imaging data evaluation value, the second judgment value of the brainstem hemorrhage imaging image quality evaluation value and the third judgment value of the vascular ultrasound guided wave monitoring accuracy evaluation value to obtain the optimization and adjustment method of vascular ultrasound guided wave monitoring.

Images