Intracranial artery positioning method and device based on transcranial Doppler ultrasound

Abstract

The invention provides an intracranial artery positioning method and device based on transcranial Doppler ultrasound. The method comprises the following steps: extracting all sampling points which contain blood flow signals and have depths within a preset range and position information of each sampling point; extracting a first type of sampling points, a second type of sampling points and a third type of sampling points from all the sampling points; performing frequency spectrum separation on the blood flow signal of each sampling point in the third type of sampling points; determining the trend of a first intracranial artery with a first flow direction and the trend of a second intracranial artery with a first flow direction; a junction location of the first intracranial artery and the second intracranial artery is determined. According to the invention, rapid rough scanning is carried out under the assistance of a mechanical device, spatial position integration of intracranial arteries in different blood flow directions is realized through classification processing of blood flow conditions of all sampling points, trends of different intracranial arteries are obtained, cross points of different intracranial arteries are determined, rapid positioning of the intracranial arteries is realized, and the positioning accuracy of the intracranial arteries is improved. And the inspection efficiency of transcranial Doppler ultrasound is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of ultrasonic data processing, and in particular, to an intracranial artery localization method, device, storage medium, and electronic device based on transcranial Doppler ultrasound. Background Art

[0002] Ultrasonic Doppler blood flow analysis is a method for evaluating the physiological characteristics of different blood flow states through non-invasive examinations. A transcranial Doppler ultrasound blood flow analyzer (TCD, Transcranial Doppler) is a customized ultrasonic device dedicated to ultrasonic examinations through the skull. Transcranial products emerged in the early 1980s and are used to diagnose cerebrovascular diseases and help examine conditions such as cerebrovascular stenosis, occlusion, poor blood flow, or cerebral hemorrhage. Applying Doppler spectrum analysis technology can provide dynamic waveforms of cerebral blood flow for clinical diagnosis, which is very important for the early detection of cerebrovascular diseases.

[0003] The ultrasonic transcranial Doppler blood flow analyzer uses an extracorporeal ultrasonic probe to emit ultrasonic waves through the gaps or "windows" of the skull. The Doppler effect (Doppler frequency shift) generated by its interaction with the blood flow is then reflected back to the probe, and the analyzer processes the data to obtain corresponding information. The main content of the Doppler effect is that the wavelength of the radiation emitted by an object changes due to the relative motion between the wave source and the observer. In front of a moving wave source, the wave is compressed, the wavelength becomes shorter, and the frequency becomes higher; behind the moving wave source, the opposite effect occurs. The wavelength becomes longer, and the frequency becomes lower; the higher the speed of the wave source, the greater the effect generated. According to the degree of change in the wave frequency, the speed at which the wave source moves along the observation direction can be calculated.

[0004] Currently, the transcranial probe requires the operator to hold it by hand to search for blood vessels. This not only involves a relatively high working intensity but also requires a high level of skill from the operator. The operator also needs to be trained and master the cerebrovascular anatomical structure to smoothly carry out the work. There are also some scanning methods assisted by mechanical devices on the market, but these devices usually have a slow search speed when performing main blood vessel localization, resulting in low clinical examination efficiency. Summary of the Invention

[0005] The purpose of the embodiments of the present disclosure is to provide an intracranial artery localization method, device, storage medium, and electronic device based on transcranial Doppler ultrasound to solve the problems of high requirements for manual scanning and low inspection efficiency of mechanical scanning in the prior art.

[0006] Embodiments of the present disclosure adopt the following technical solutions: A method for intracranial artery localization based on transcranial Doppler ultrasound, comprising: performing three-dimensional scanning on a preset detection window on a patient's head through a single-element ultrasound probe, extracting all sampling points within a sampling volume that contain blood flow signals and whose depth is within a preset range, and the position information of each of the sampling points; extracting first-class sampling points, second-class sampling points, and third-class sampling points from all the sampling points; wherein, the blood flow signal of the first-class sampling points only has a first flow direction, the blood flow signal of the second sampling points only has a second flow direction, the energy feature of the blood flow signal of the third-class sampling points is greater than an energy threshold, and the first flow direction is opposite to the second flow direction; performing spectral separation on the blood flow signal of each sampling point in the third-class sampling points to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction; determining the trend of the first intracranial artery with the first flow direction according to all the vascular positions with the first flow direction and the position information of all the first-class sampling points, and determining the trend of the second intracranial artery with the second flow direction according to all the vascular positions with the second flow direction and the position information of all the second-class sampling points; determining the intersection position of the first intracranial artery and the second intracranial artery according to the trend of the first intracranial artery and the trend of the second intracranial artery.

[0007] Embodiments of the present disclosure also provide an intracranial artery localization device based on transcranial Doppler ultrasound, comprising: a sampling module, configured to perform three-dimensional scanning on a preset detection window on a patient's head through a single-element ultrasound probe, and extract all sampling points within a sampling volume that contain blood flow signals and whose depth is within a preset range, and the position information of each of the sampling points; a classification module, configured to extract first-class sampling points, second-class sampling points, and third-class sampling points from all the sampling points; wherein, the blood flow signal of the first-class sampling points only has a first flow direction, the blood flow signal of the second sampling points only has a second flow direction, the energy feature of the blood flow signal of the third-class sampling points is greater than an energy threshold, and the first flow direction is opposite to the second flow direction; a spectral separation module, configured to perform spectral separation on the two blood flow signals of each sampling point in the third-class sampling points to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction; a localization module, configured to determine the trend of the first intracranial artery with the first flow direction according to all the vascular positions with the first flow direction and the position information of all the first-class sampling points, and determine the trend of the second intracranial artery with the second flow direction according to all the vascular positions with the second flow direction and the position information of all the second-class sampling points; determining the intersection position of the first intracranial artery and the second intracranial artery according to the trend of the first intracranial artery and the trend of the second intracranial artery.

[0008] An embodiment of the present disclosure also provides a storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned intracranial artery localization method based on transcranial Doppler ultrasound are implemented.

[0009] An embodiment of the present disclosure also provides an electronic device including at least a memory and a processor. A computer program is stored on the memory, and when the processor executes the computer program on the memory, the steps of the above-mentioned intracranial artery localization method based on transcranial Doppler ultrasound are implemented.

[0010] The beneficial effects of the embodiments of the present disclosure are as follows: By using a mechanical device to assist in rapid rough scanning and classifying the blood flow conditions at each sampling point, the spatial positions of intracranial arteries with different blood flow directions are integrated, thereby obtaining the trend of different intracranial arteries, determining the intersection points of different intracranial arteries, and realizing rapid localization of intracranial arteries without complex operations such as three-dimensional modeling, effectively improving the examination efficiency of transcranial Doppler ultrasound. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is the intracranial artery localization method based on transcranial Doppler ultrasound provided for one or more embodiments of this specification;

[0013] Figure 2 It is the structural schematic diagram of the transcranial Doppler device provided for one or more embodiments of this specification;

[0014] Figure 3 It is the schematic diagram of the cerebral arterial circle provided for one or more embodiments of this specification;

[0015] Figure 4(a) is the schematic diagram of multi-angle one-dimensional scanning of the mechanical device provided for one or more embodiments of this specification;

[0016] Figure 4(b) is the schematic diagram of multi-angle two-dimensional scanning of the mechanical device provided for one or more embodiments of this specification;

[0017] Figure 5 It is the schematic diagram of tangent scanning provided for one or more embodiments of this specification;

[0018] Figure 6 It is the interference schematic diagram provided for one or more embodiments of this specification;

[0019] Figure 7 Schematic diagrams of sampling two blood vessels provided for one or more embodiments of this specification;

[0020] Figure 8 Schematic diagram of scan line sampling provided for one or more embodiments of this specification;

[0021] Figure 9 Schematic diagram of cross - position prediction provided for one or more embodiments of this specification;

[0022] Figure 10 Schematic diagram of the structure of an intracranial artery positioning device based on transcranial Doppler ultrasound provided for one or more embodiments of this specification. Detailed implementation manners

[0023] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0024] Ultrasonic Doppler blood flow analysis is a method for evaluating the physiological characteristics of different blood flow states through non - invasive examinations. A transcranial Doppler ultrasound blood flow analyzer (TCD, Transcranial Doppler) is a customized ultrasonic device specifically used for ultrasonic examinations through the skull. Transcranial Doppler emerged in the early 1980s and is used to diagnose cerebrovascular diseases, helping to examine conditions such as cerebrovascular stenosis, blockage, poor blood flow, or cerebral hemorrhage. By applying Doppler spectral analysis technology, it can provide dynamic waveforms of cerebral blood flow for clinical diagnosis, which is very important for the early detection of cerebrovascular diseases.

[0025] An ultrasonic transcranial Doppler blood flow analyzer uses an extracorporeal ultrasonic probe to emit ultrasonic waves to the cerebral blood vessels through the gaps or "windows" of the skull. The Doppler effect (Doppler frequency shift) generated by its interaction with the blood flow is then reflected back to the probe, and the analyzer processes the data to obtain corresponding information. The main content of the Doppler effect is that the wavelength of the radiation of an object changes due to the relative motion between the wave source and the observer. In front of a moving wave source, the wave is compressed, the wavelength becomes shorter, and the frequency becomes higher; behind a moving wave source, the opposite effect occurs. The wavelength becomes longer, and the frequency becomes lower; the higher the speed of the wave source, the greater the effect generated. According to the degree of change in the wave frequency, the speed at which the wave source moves along the observation direction can be calculated.

[0026] Currently, for transcranial probes, the operator needs to hold the probe to search for blood vessels. This not only involves a relatively high working intensity, but also requires high skills from the operator. The operator also needs to be trained and master the cerebrovascular anatomical structure to smoothly carry out the work. Some scanning methods assisted by mechanical devices have also emerged on the market, but these devices usually have a slow search speed when positioning the main blood vessels, resulting in low clinical examination efficiency.

[0027] To solve the above problems, the first embodiment of the present disclosure provides a method for intracranial artery localization based on transcranial Doppler ultrasound. The flowchart is as shown in Figure 1 and can be specifically implemented by any electronic device with data processing functions, mainly including steps S10 to S50:

[0028] S10, perform three-dimensional scanning on a preset detection window on the patient's head through a single-element ultrasonic probe, and extract all sampling points within the sampling volume that contain blood flow signals and whose depth is within a preset range, as well as the position information of each said sampling point.

[0029] Figure 2 FIG. shows a schematic structural diagram of the transcranial Doppler device used in this embodiment. It consists of a transmission control unit, a driving unit, a probe holder, a probe, an amplifier, an AD sampling unit, a demodulation unit, a signal processing unit, and a display and sound unit. The transmission control unit provides a specific transmission signal and transmits it at a certain frequency; the driving unit converts the transmission signal into high voltage to drive the probe for electro-acoustic conversion; the probe holder is the physical connection between the main unit and the probe, and generally, a single main unit can be connected to multiple different probes; when ultrasonic waves are sent into human tissues, a part of the energy will return to the probe; the probe then performs acoustic-electric conversion to convert the echo containing tissue information back into an electrical signal again; the amplifier converts the weak electrical signal into a stronger electrical signal, and then through the AD sampling unit, converts the analog electrical signal into a digital signal; the demodulation unit and the signal processing unit remove the useless noise in the echo signal and only retain the effective Doppler signal, and finally convert it into an image and sound to feedback to the operator. Each transmission is equivalent to performing a sampling on the time axis, and this transmission frequency is the sampling rate (Fs). The original data collected is a one-dimensional signal f(t) that changes with time. The Doppler signal is essentially a non-stationary signal, mainly reflecting the frequency domain characteristics, and the time-varying frequency will also change accordingly.

[0030] It should be noted that the electronic device executing the method of this embodiment has the function of communicating with the Figure 2 transcranial Doppler device shown in FIG., and it can receive the Doppler signal output by the signal processing unit, and can also communicate with the transmission control unit and the driving unit to set scanning parameters such as the scanning position and scanning angle of this scan.

[0031] The most important blood vessels inside the brain form a ring-like structure that connects the two hemispheres and the anterior and posterior circulations, called the cerebral arterial circle (Circle of Willis). As shown in Figure 3 , the arteries in the brain are symmetric left and right. The main arteries include the anterior cerebral artery, middle cerebral artery, P1 segment of the posterior artery, P2 segment of the posterior artery, basilar artery, and vertebral artery, etc. Since the brain is protected by the skull, and the skull is not conducive to the transmission of ultrasonic signals, it is necessary to find relatively thin places on the skull to ensure that the signals can be correctly obtained. Typical checkpoints are the temporal window ( Figure 3 , checkpoint 1 in the figure) and the occipital window ( Figure 3 , checkpoint 2 in the figure). When examining from the temporal window, usually the anterior artery, middle artery, and posterior artery can be examined. The corresponding blood flow directions are that the anterior artery is away from the probe, the middle artery is towards the probe, the P1 segment of the posterior artery is towards the probe, and the P2 segment is away from the probe; when examining from the occipital window, usually the basilar artery and vertebral artery can be examined. The corresponding blood flow directions are that the basilar artery is away from the probe and the vertebral artery is away from the probe. The blood flow direction is an important feature in TCD examination. If the blood flow direction is different from the normal direction, it can be used as an important basis for diagnosis. The preset detection window selected during the automated patient head scan in this embodiment is the temporal window, used to achieve the rapid positioning of the anterior cerebral artery, middle cerebral artery, and their intersection point.

[0032] During the actual scanning, due to the obstruction of the skull, penetrating the skull will cause a large amount of signal loss. Therefore, in this embodiment, a single-element probe is used for examination. This single-element probe has a relatively large diameter (typically 2 cm), and at the same time, a relatively large sampling volume is used (typically 2 cm). Its typical sampling space is close to a sphere. Although a relatively large sampling volume will result in poor spatial resolution of the image, during the rapid vascular screening process, a relatively large sampling volume enables sampling of points near the target intracranial artery during each sampling process, thereby improving the examination efficiency. Further, because there is only one element, when scanning in cooperation with a mechanical device, multi-angle scanning can be performed as shown in FIGS. 4(a) and 4(b). At the same time, mid- to high-end TCD already supports multi-depth scanning in the dimension of the probe extension line (z dimension). Therefore, by cooperating with the mechanical device to achieve changes in the x and y dimensions and completing the z-direction scanning through electronic scanning, three-dimensional scanning can be achieved, and the corresponding spatial positions of each scanned point are determined during the scanning process. It should be noted that in this embodiment, the judgment can actually be based on the results of the electronic scanning. For the results of the electronic scanning, the corresponding spatial positions are determined based on the coordinate system of the scanning device.

[0033] In this embodiment, for all scanned points, points with blood flow signals and depths within a preset range are selected from the Doppler signals fed back by the TCD device as sampling points, and the position information of each sampling point is obtained at the same time. The preset range is mainly set according to the type of artery actually located. In this embodiment, the main detection targets are the anterior cerebral artery and the middle cerebral artery, and the corresponding preset range can be set between 60 and 64 mm. Blood flow signals not falling within this depth range may be signals from other blood vessels, and this embodiment does not process them.

[0034] In addition, during the actual scanning process, the probe scans quickly along the tangent direction of the blood vessel. As Figure 5 shown, each scanned point stays for a short time and a quick scan can be performed. It should be noted that although the scanning time for each point is short, in order to avoid interference, the scanning time can be set to be greater than a threshold. A common interference in TCD is impulse response interference. Therefore, if it is assumed that there may be an interference during the acquisition time, at least it should be ensured that when the interference occurs at any time point, a complete interference-free signal can be acquired in one frame. Figure 6 Illustrates a typical interference situation in this embodiment. If the interference in Display Frame 1 is to be completely avoided, an independent Display Frame 4 needs to be obtained, and the data of Display Frame 4 does not overlap with that of Display Frame 1. Only when there is good repeatability and no mutation in these 4 frames can a stable signal be determined. If they are inconsistent, the signal can be determined only when two independent display frames have repeatability.

[0035] S20. Extract the first type of sampling points, the second type of sampling points, and the third type of sampling points from all the sampling points.

[0036] Combined with the blood flow direction and energy characteristics of the intracranial arteries that actually need to be located in this embodiment, all the sampling points are classified. Among them, the blood flow signal of the first type of sampling points only has the first flow direction, the blood flow signal of the second sampling points only has the second flow direction, and the energy characteristic of the blood flow signal of the third type of sampling points is greater than the energy threshold. It should be noted that due to the large sampling volume, there are many points that simultaneously contain the information of the anterior artery and the middle artery. Therefore, there are a large number of sampling points that simultaneously sample the anterior artery and the middle artery on the extension line of the probe. As Figure 7 shown, since the blood directions of the anterior artery and the middle artery are opposite, the combined flow velocity of this sampling point may be any value during scanning. Using the flow velocity as a threshold cannot determine the blood flow direction of this point. However, since the energy in the overlapping area of the anterior artery region and the middle artery region on the scanning extension line will contain the energies of the two blood vessel signals, it can be identified according to its energy characteristics. As Figure 8As shown, when the energy feature of the blood flow signal exceeds the energy threshold, it can be determined that there are two blood flow signals with opposite directions at the current sampling point, and such sampling points are regarded as the third type of sampling points.

[0037] Each type of sampling point in this embodiment can have multiple sampling points, and each sampling point may correspond to different positions of the intracranial artery to be located. The first blood flow direction and the second blood flow direction in this embodiment are opposite directions, corresponding to the anterior cerebral artery and the middle cerebral artery that need to be located. When the probe scans through the temporal window, the blood flow direction of the anterior cerebral artery is away from the probe, and the blood flow direction of the middle cerebral artery is towards the probe.

[0038] S30. Perform spectral separation on the blood flow signals of each sampling point in the third type of sampling points to obtain the vascular positions with the first blood flow direction and the vascular positions with the second blood flow direction.

[0039] In this embodiment, spectral separation is performed on each sampling point in the third type of sampling points with two blood flow signals having opposite directions to separate the two blood flow signals, providing a positioning basis for subsequent artery positioning. In some embodiments, the signals of each third type of sampling point can be processed based on the fast Fourier transform (FFT). Specifically, first, the FFT is applied to convert the time series data of each sampling point, converting the time-domain signal into a frequency-domain representation to obtain a spectrogram, where the frequency axis represents blood flows at different speeds, and the intensity axis reflects the signal intensity at the corresponding speed. Subsequently, since the blood flow directions and speeds of the two blood vessels are different, they will form different frequency offset components on the spectrogram. For example, the blood flow in one blood vessel may move towards the probe (positive direction), and the blood flow in the other blood vessel may move away from the probe (negative direction). Based on this, these two frequency offset components can be automatically identified and classified by setting a threshold or using a machine learning algorithm. Corresponding to the above two frequency offset components, the average flow velocity and energy can be calculated to determine the specific flow characteristics inside each blood vessel, such as the depth of the center line of the corresponding blood vessel, and then the vascular positions with the first blood flow direction and the vascular positions with the second blood flow direction can be obtained.

[0040] In some embodiments, spectral separation can also be performed by filtering in combination with the autocorrelation algorithm. Specifically, filter the time series data of each sampling point in the third type of sampling points, formulate a filtering strategy according to the characteristics of two blood vessels with opposite blood flow directions, and then the time series data with the first blood flow direction and the time series data with the second blood flow direction can be separated; subsequently, perform autocorrelation analysis on the time series data with the first blood flow direction and the time series data with the second blood flow direction based on the autocorrelation algorithm. For periodic blood flow signals, the autocorrelation function will show peaks at certain delay values, and these peaks are related to the blood flow velocity. Analyze the peak positions in the autocorrelation function to determine the most significant time delay. Using the time delay information obtained from the autocorrelation function, combined with the ultrasonic propagation velocity and the Doppler angle, the average blood flow velocity can be calculated, and at the same time, the energy situation can be evaluated by integrating the change of the signal intensity (or power) over time; finally, according to the average blood flow velocity and energy of each time series data, determine the centerline depth of the blood vessel corresponding to each time series data, and obtain the positions of the blood vessels with the first blood flow direction and the positions of the blood vessels with the second blood flow direction.

[0041] S40. Determine the orientation of the first intracranial artery with the first blood flow direction according to the positions of all the blood vessels with the first blood flow direction and the position information of all the first type of sampling points, and determine the orientation of the second intracranial artery with the second blood flow direction according to the positions of all the blood vessels with the second blood flow direction and the position information of all the second type of sampling points.

[0042] After performing spectral separation processing on the third type of sampling points, the position information of all the blood vessel signals with the first blood flow direction and the position information of all the blood vessel signals with the second blood flow direction during the current scanning process can be determined. Among them, the position information of all the blood vessel signals with the first blood flow direction includes the positions of the blood vessels with the first blood flow direction obtained by spectral separation of the third type of signals, and the position information of the first type of sampling points with only the blood flow signals with the first blood flow direction; similarly, the position information of all the blood vessel signals with the second blood flow direction includes the positions of the blood vessels with the second blood flow direction obtained by spectral separation of the third type of signals, and the position information of the second type of sampling points with only the blood flow signals with the second blood flow direction. Integrate the position information of the above sampling points according to the blood flow direction, and the position information of the first intracranial artery with the first blood flow direction and the position information of the second intracranial artery with the second blood flow direction can be obtained, and then the orientations of the first intracranial artery and the second intracranial artery can be determined.

[0043] It should be noted that this embodiment does not stipulate whether the first blood flow direction is the direction away from the probe or the direction towards the probe. When actually implementing the method of this embodiment, the first blood flow direction can be any one of the above two directions, and the second blood flow direction is just opposite to the first blood flow direction; similarly, the first intracranial artery is correspondingly determined as the anterior cerebral artery or the middle cerebral artery according to the specific direction of the first blood flow direction.

[0044] S50. Determine the intersection position of the first intracranial artery and the second intracranial artery according to the orientations of the first intracranial artery and the second intracranial artery.

[0045] After determining the orientations of the first intracranial artery and the second intracranial artery, the intersection position between the two can be determined. The position of the above intersection is a key reference point in the Willis circle and is the basis for locating other arteries. After determining the positions of the first intracranial artery, the second intracranial artery, and the intersection, the position prediction of other intracranial arteries can be carried out.

[0046] In some embodiments, simple linear prediction can be used. The intersection position between the first intracranial artery and the second intracranial artery can be predicted linearly using the orientations of the first intracranial artery and the second intracranial artery, as Figure 9 shown. In the actual prediction process, a cross-point prediction model can also be established. The positions of all blood vessels with the first flow direction, the position information of all the first-type sampling points, the positions of all blood vessels with the second flow direction, and the position information of all the second-type sampling points are substituted into the cross-point prediction model, and the model solution in the least squares sense is obtained by the least squares method, thereby obtaining the predicted cross-point position.

[0047] In some embodiments, based on the orientations of the first intracranial artery and the second intracranial artery, more accurate judgment of the intersection position can be made by combining other effective information. For example, the current orientations of the first intracranial artery and the second intracranial artery are matched with a preset three-dimensional brain blood flow structure template, and the intersection position of the first intracranial artery and the second intracranial artery under the preset three-dimensional brain blood flow structure template is determined according to the matching result. It should be noted that due to differences in gender, age, etc. of each patient, there will be certain differences in the three-dimensional brain blood flow structure. Therefore, there can be one or more preset three-dimensional brain blood flow structure templates. In actual matching, different preset three-dimensional brain blood flow structure templates can be matched separately, and the preset three-dimensional brain blood flow structure template with the highest matching degree can be determined for cross-point position determination.

[0048] After determining the intersection position by the method of this embodiment, the probe can be further controlled to scan at the intersection position for at least one cardiac cycle. By detecting whether the spectral cycle change and peak flow velocity energy at the intersection position during the cardiac cycle meet the preset optimal conditions, to further evaluate whether the current intersection position is the optimal position. If the detection of the current intersection position meets the preset optimal conditions, it is determined that the current intersection position is the optimal position, and the determination of other blood vessel positions can be based on this optimal position. If it does not meet the conditions, the method of this embodiment can be re-executed until the optimal intersection position is determined. After determining the intersection position, according to the prior knowledge of the Willis circle, combined with the preset three-dimensional brain blood flow structure template, the intersection positions between other intracranial arteries can be determined, so as to determine the intracranial arteries, especially the positions of the main arteries on the Willis circle, so as to achieve a fast and accurate output of the intracranial artery positioning result.

[0049] This embodiment uses a mechanical device to assist in rapid rough scanning, and through the classification processing of the blood flow conditions at each sampling point, realizes the spatial position integration of the intracranial arteries with different blood flow directions, thereby obtaining the trend of different intracranial arteries, determining the intersections of different intracranial arteries, and realizing the rapid positioning of intracranial arteries without complex operations such as three-dimensional modeling, effectively improving the inspection efficiency of transcranial Doppler ultrasound.

[0050] Based on the same inventive concept, the second embodiment of the present disclosure provides an intracranial artery positioning device based on transcranial Doppler ultrasound, and its structural schematic diagram is as Figure 10As shown, the positioning device mainly includes: a sampling module 10, configured to perform three-dimensional scanning on a preset detection window on the patient's head through a single-element ultrasonic probe, and extract all sampling points within a sampling volume that contain blood flow signals and whose depths are within a preset range, as well as the position information of each of the sampling points; a classification module 20, configured to extract first-class sampling points, second-class sampling points, and third-class sampling points from all the sampling points; wherein, the blood flow signal of the first-class sampling points only has a first flow direction, the blood flow signal of the second sampling points only has a second flow direction, the energy feature of the blood flow signal of the third-class sampling points is greater than an energy threshold, and the first flow direction is opposite to the second flow direction; a spectrum separation module 30, configured to perform spectrum separation on the blood flow signal of each sampling point in the third-class sampling points to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction; a positioning module 40, configured to determine the trend of the first intracranial artery with the first flow direction according to all the vascular positions with the first flow direction and the position information of all the first-class sampling points, and determine the trend of the second intracranial artery with the second flow direction according to all the vascular positions with the second flow direction and the position information of all the second-class sampling points; and determine the intersection position of the first intracranial artery and the second intracranial artery according to the trend of the first intracranial artery and the trend of the second intracranial artery. Specifically, the preset detection window is the temporal window, the first intracranial artery is the anterior cerebral artery, and the second intracranial artery is the middle cerebral artery.

[0051] In some embodiments, the spectrum separation module 30 is specifically configured to: convert the time series data of each sampling point in the third-class sampling points into a spectrogram based on the fast Fourier transform; obtain two frequency offset components from the spectrogram, and determine the average flow velocity and energy of the corresponding blood vessels according to each frequency offset component; and determine the centerline depth of the blood vessels corresponding to each frequency offset component according to the average flow velocity and energy of each frequency offset component, so as to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction.

[0052] In some embodiments, the spectrum separation module 30 is specifically configured to: filter the time series data of each sampling point in the third-class sampling points to obtain the time series data with the first flow direction and the time series data with the second flow direction; perform autocorrelation analysis on the time series data with the first flow direction and the time series data with the second flow direction based on the autocorrelation algorithm to determine the average flow velocity and energy of each time series data; and determine the centerline depth of the blood vessels corresponding to each time series data according to the average flow velocity and energy of each time series data, so as to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction.

[0053] In some embodiments, the positioning module 40 is specifically configured to: establish an intersection prediction model, substitute the positions of all blood vessels with a first blood flow direction, the position information of all first-type sampling points, the positions of all blood vessels with a second blood flow direction, and the position information of all second-type sampling points into the intersection prediction model, and obtain the model solution in the sense of least squares to obtain the predicted intersection position.

[0054] In some embodiments, the positioning module 40 is specifically configured to: determine the intersection position of the first intracranial artery and the second intracranial artery under a preset three-dimensional cerebral blood flow structure template according to the trends of the first intracranial artery and the second intracranial artery.

[0055] In some embodiments, the positioning module 40 is further configured to: determine the intersection positions between other intracranial arteries according to the intersection position of the first intracranial artery and the second intracranial artery and the preset three-dimensional cerebral blood flow structure template.

[0056] In some embodiments, the positioning module 40 is further configured to: perform three-dimensional scanning of the intersection position for at least one cardiac cycle through a single-element ultrasonic probe, and detect whether the spectral cycle change and peak flow velocity energy of the intersection position during the cardiac cycle meet preset optimal conditions. If they meet, determine that the intersection position is the optimal position.

[0057] This embodiment uses a mechanical device to assist in rapid rough scanning, and through the classification processing of the blood flow conditions of each sampling point, realizes the spatial position integration of intracranial arteries with different blood flow directions, thereby obtaining the trend of the intracranial arteries with different directions, determining the intersections of different intracranial arteries, and realizing the rapid positioning of intracranial arteries without complex operations such as three-dimensional modeling, effectively improving the inspection efficiency of transcranial Doppler ultrasound.

[0058] Based on the same inventive concept, the third embodiment of the present disclosure provides a storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the intracranial artery positioning method based on transcranial Doppler ultrasound provided by the first embodiment are implemented.

[0059] Based on the same inventive concept, the fourth embodiment of the present disclosure provides an electronic device including at least a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program on the memory, the steps of the intracranial artery positioning method based on transcranial Doppler ultrasound provided by the first embodiment are implemented.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than limiting them; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for intracranial artery localization based on transcranial Doppler ultrasound, characterized in that, Including: Performing three-dimensional scanning on a preset detection window on a patient's head through a single-element ultrasonic probe, extracting all sampling points within a sampling volume that contain blood flow signals and whose depth is within a preset range, and the position information of each of the sampling points; Extracting first-class sampling points, second-class sampling points, and third-class sampling points from all the sampling points; wherein, the blood flow signal of the first-class sampling points only has a first flow direction, the blood flow signal of the second sampling points only has a second flow direction, the energy characteristic of the blood flow signal of the third-class sampling points is greater than an energy threshold, and the first flow direction is opposite to the second flow direction; Performing spectral separation on the blood flow signal of each sampling point in the third-class sampling points to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction; Determining the trend of the first intracranial artery with the first flow direction according to all the vascular positions with the first flow direction and the position information of all the first-class sampling points, and determining the trend of the second intracranial artery with the second flow direction according to all the vascular positions with the second flow direction and the position information of all the second-class sampling points; Determining the intersection position of the first intracranial artery and the second intracranial artery according to the trend of the first intracranial artery and the trend of the second intracranial artery.

2. The intracranial artery localization method according to claim 1, characterized in that, The performing spectral separation on the two blood flow signals of each sampling point in the third-class sampling points to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction includes: Converting the time series data of each sampling point in the third-class sampling points into a spectrogram based on the fast Fourier transform; obtaining two frequency offset components according to the spectrogram, and determining the average flow velocity and energy of the corresponding blood vessels according to each frequency offset component; determining the center line depth of the blood vessels corresponding to each frequency offset component according to the average flow velocity and energy of each frequency offset component, to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction.

3. The intracranial artery localization method according to claim 1, wherein, The performing spectral separation on the two blood flow signals of each sampling point in the third-class sampling points to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction includes: Filtering the time series data of each sampling point in the third-class sampling points to obtain the time series data with the first flow direction and the time series data with the second flow direction; performing autocorrelation analysis on the time series data with the first flow direction and the time series data with the second flow direction based on the autocorrelation algorithm to determine the average flow velocity and energy of each time series data; determining the center line depth of the blood vessels corresponding to each time series data according to the average flow velocity and energy of each time series data, to obtain the vascular positions with the first flow direction and the vascular positions with the second flow direction.

4. The intracranial artery localization method according to claim 1, characterized in that, The determining the intersection position of the first intracranial artery and the second intracranial artery according to the trend of the first intracranial artery and the trend of the second intracranial artery includes: A cross-point prediction model is established. The positions of all blood vessels with a first blood flow direction, the position information of all the first-type sampling points, the positions of all blood vessels with a second blood flow direction, and the position information of all the second-type sampling points are substituted into the cross-point prediction model, and the model solution in the sense of least squares is obtained to get the predicted cross-point position.

5. The intracranial artery localization method according to claim 1, wherein, The determination of the cross-point position between the first intracranial artery and the second intracranial artery according to the directions of the first intracranial artery and the second intracranial artery includes: According to the directions of the first intracranial artery and the second intracranial artery, determine the cross-point position between the first intracranial artery and the second intracranial artery under a preset three-dimensional brain blood flow structure template.

6. The intracranial artery localization method according to claim 5, wherein After determining the cross-point position between the first intracranial artery and the second intracranial artery according to the directions of the first intracranial artery and the second intracranial artery, it further includes: According to the cross-point position between the first intracranial artery and the second intracranial artery and the preset three-dimensional brain blood flow structure template, determine the cross-point positions between other intracranial arteries.

7. The intracranial artery localization method according to any one of claims 1 to 6, characterized in that After determining the cross-point position between the first intracranial artery and the second intracranial artery, it further includes: Perform three-dimensional scanning of the cross-point position for at least one cardiac cycle by a single-element ultrasound probe, and detect whether the spectral cycle change and peak flow velocity energy at the cross-point position within the cardiac cycle meet the preset optimal conditions. If they meet, determine the cross-point position as the optimal position.

8. An intracranial artery positioning device based on transcranial Doppler ultrasound, characterized in that, It includes: A sampling module for performing three-dimensional scanning through a single-element ultrasound probe at a preset detection window on the patient's head, and extracting all sampling points within the sampling volume that contain blood flow signals and whose depth is within a preset range, as well as the position information of each sampling point; A classification module for extracting first-type sampling points, second-type sampling points, and third-type sampling points from all the sampling points; wherein, the blood flow signal of the first-type sampling points only has a first blood flow direction, the blood flow signal of the second sampling points only has a second blood flow direction, the energy feature of the blood flow signal of the third-type sampling points is greater than an energy threshold, and the first blood flow direction is opposite to the second blood flow direction; A spectral separation module for performing spectral separation on the blood flow signal of each sampling point in the third-type sampling points to obtain the positions of blood vessels with a first blood flow direction and the positions of blood vessels with a second blood flow direction; A positioning module for determining the direction of the first intracranial artery with a first blood flow direction according to the positions of all blood vessels with a first blood flow direction and the position information of all the first-type sampling points, and determining the direction of the second intracranial artery with a second blood flow direction according to the positions of all blood vessels with a second blood flow direction and the position information of all the second-type sampling points; according to the directions of the first intracranial artery and the second intracranial artery, determine the cross-point position between the first intracranial artery and the second intracranial artery.

9. A storage medium stores a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the intracranial artery positioning method based on transcranial Doppler ultrasound according to any one of claims 1 to 7.

10. An electronic device, at least comprising a memory and a processor, wherein a computer program is stored on the memory, characterized in that, When executing the computer program on the memory, the processor implements the steps of the transcranial Doppler ultrasound-based intracranial artery localization method according to any one of claims 1 to 7.

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