Intelligent adjusting method of head fixing device for radiography based on cerebrovascular orientation recognition

Abstract

The invention discloses an intelligent adjusting method for a head fixing device for radiography based on cerebrovascular orientation recognition, and relates to the technical field of medical diagnosis assistance. The method comprises the following steps: acquiring head CT or MRI image data before radiography, extracting cerebrovascular anatomical features and head contour data, and constructing a three-dimensional model and a personalized anatomical database; a fixing device is installed and preliminarily adjusted to be attached to the head according to the database, and initial contact pressure data are collected; real-time images and head movement data are dynamically collected in radiography, the brain blood vessel orientation is fused and tracked, and the posture offset is calculated; a multi-degree-of-freedom adjusting mechanism is driven, and the position and angle compensation deviation of the device are adjusted in real time; pressure distribution and posture are monitored, automatic adjustment is performed when a threshold value is exceeded, and related data are recorded to form a log; after radiography, the device is reset step by step, and various data are associated and stored to provide reference for subsequent adjustment. Dynamic and accurate adjustment is realized through personalized modeling and real-time tracking, so that the cerebral vessels are always in the optimal imaging direction.

Description

Technical Field

[0001] This invention relates to the field of medical diagnostic auxiliary technology, and in particular to an intelligent adjustment method for angiography head fixation devices based on cerebral vascular orientation recognition. Background Technology

[0002] Cerebral angiography is the gold standard for the clinical diagnosis of cerebrovascular diseases, and its imaging quality directly determines the accuracy of lesion detection and diagnosis. During the angiography process, the patient's head needs to remain in a fixed position for a long time to ensure that the cerebral blood vessels are always in the optimal imaging position. This avoids blurring or overlapping of vascular images due to slight head movements, which would affect the doctor's judgment of the location, size, and shape of the lesions. Therefore, the stability, fit, and precise adjustment of the head fixation device are key factors in ensuring the success of cerebral angiography.

[0003] Current head fixation devices for cerebral angiography mostly employ traditional mechanical structures, which have numerous technical limitations. These devices are typically standardized in size, lacking the ability to be personalized to suit individual patient anatomical characteristics. Initial fixation can only be achieved through manual adjustment of the support height and the tightness of the fixation straps. Because there are significant differences in head contours, skull morphology, and cerebral vascular orientation among different patients, standardized fixation methods are prone to insecure fixation or excessive local pressure. This can not only cause imaging distortion due to unconscious head movement during angiography, but also lead to discomfort such as headaches and poor blood circulation, reducing patient compliance. Furthermore, manual adjustment relies on the clinical experience of medical staff, with limited precision, making it difficult to accurately match the spatial orientation of cerebral blood vessels. This is especially true for patients with complex cerebral vascular branches or lesions in unusual locations, where empirical adjustments are insufficient to achieve optimal imaging positioning.

[0004] Furthermore, existing fixation devices lack dynamic adjustment and feedback mechanisms, making them unable to handle unexpected situations during angiography. Angiography is time-consuming, and patients may experience slight head posture shifts due to fatigue or physiological reactions. Traditional fixation devices, once fixed, cannot be adjusted autonomously, requiring medical staff to interrupt the examination for manual correction, which prolongs the examination time and increases the risk of radiation exposure. Simultaneously, the contact pressure between the device and the patient's head lacks effective monitoring and control. Excessive local pressure may compress scalp blood vessels or nerves, causing discomfort or even complications; insufficient pressure cannot guarantee fixation stability. More importantly, existing devices are not linked to medical imaging data, making it impossible to adjust them based on real-time changes in the location of cerebral blood vessels. This makes it difficult to achieve precise matching between "vascular location and fixation posture," resulting in poor angiography quality in some complex cases and affecting the accuracy of clinical diagnosis. These technical shortcomings severely restrict the efficiency and quality of cerebral angiography, urgently requiring an intelligent adjustment and fixation method based on cerebral blood vessel location recognition to address the core needs of personalized adaptation, dynamic stability, and accurate imaging. Summary of the Invention

[0005] The present invention proposes an intelligent adjustment method for angiography head fixation device based on cerebral vascular orientation recognition to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent adjustment method for a head fixation device for angiography based on cerebral vascular orientation recognition, comprising:

[0007] Three-dimensional modeling of cerebral vascular orientation before angiography: CT or MRI images of the patient's head are acquired using medical imaging equipment, and the anatomical features of the cerebral vascular system, the direction of cerebral vascular branches, the coordinates of vascular nodes, and the relative positional relationship between the blood vessels and the skull are extracted. A digital model of cerebral vascular orientation is constructed using a three-dimensional reconstruction algorithm. At the same time, the patient's head contour dimensions and skull landmark location data are acquired to establish a patient head anatomy database. Initial posture calibration of the fixation device: The head fixation device is installed on the contrast examination table. Based on the patient's personalized head anatomy database, the height of the support base, the opening angle of the side fixation arm, and the position of the top pressure pad are adjusted to make the device initially fit the contour of the patient's head. The initial contact pressure data is collected through the pressure sensor built into the device to make the pressure distribution uniform and within the safe threshold range. Real-time cerebrovascular orientation tracking and localization: During the angiography process, the image guidance system dynamically acquires real-time head images, extracts the dynamic coordinate information of cerebrovascular nodes, registers them with the three-dimensional model constructed before the operation, calculates the head posture offset, translational deviation and rotational deviation, and at the same time, the device's built-in inertial measurement unit acquires head motion posture data to achieve data fusion tracking of cerebrovascular orientation. Intelligent dynamic adjustment of the fixation device: Based on the cerebral blood vessel orientation tracking results and posture offset, the fixation device's degree of freedom adjustment mechanism is driven by the horizontal translation module, vertical lifting module, and rotation adjustment module to adjust the position and angle of the support base, side fixation arm, and top pressure pad in real time to compensate for head posture offset and keep the cerebral blood vessels in the best imaging position for angiography. Pressure and posture closed-loop control during adjustment: Real-time monitoring of pressure distribution data between the fixation device and the head; when the pressure exceeds the safety threshold, the support strength of the corresponding part is automatically adjusted; when the posture deviation exceeds the allowable range, the emergency adjustment mechanism is activated to quickly correct the head position, and posture data, pressure data and cerebrovascular location data are recorded during the adjustment process to form an adjustment log. After the angiography is completed, the device is reset and the data is stored. After the angiography is completed, the fixation device gradually loosens the fixation mechanism according to the preset program and slowly resets to the initial state without causing impact to the patient's head. The preoperative modeling data, intraoperative adjustment data, and angiography imaging quality feedback data are stored together.

[0008] Furthermore, it also includes: Steps for fitting the central axis of cerebral blood vessels: By extracting the coordinates of feature points in cerebral vascular images, the central axis of cerebral blood vessels is constructed using a least-squares fitting algorithm. The fitting formula is as follows: ,in , , , The general parameters for the plane containing the central axis of cerebral blood vessels. , , For the first Three-dimensional coordinates of cerebral vascular feature points The total number of feature points is used to determine the spatial orientation of cerebral blood vessels through fitting results, providing an orientation reference for the initial positioning of the fixation device.

[0009] The steps for calculating personalized head support parameters for patients are as follows: By combining the patient's head contour dimensions, the location of skull landmarks and the orientation of cerebral blood vessels, the optimal support force and angle parameters of each support part of the fixation device are calculated. The support force adopts a segmented adjustment strategy, and different pressure thresholds are set according to the anatomical characteristics of different areas of the head. At the same time, the distribution of support points is optimized according to the relative position of key cerebral blood vessel nodes and the support points of the fixation device.

[0010] Furthermore, it also includes: Image-guided dynamic registration optimization steps: The iterative nearest point algorithm is used to optimize the registration between the preoperative 3D model and the intraoperative real-time image. After each registration, the registration error is calculated. When the registration error is greater than the preset threshold, feature points are extracted again for registration iteration until the registration error meets the requirements of angiography. The registration priority of nodes is improved by weighted registration strategy.

[0011] Furthermore, it also includes a head posture deviation compensation algorithm. When a head posture deviation is detected, the algorithm calculates the mapping relationship between the deviation and the adjustment amount to achieve compensation adjustment of the fixation device. The compensation formula is as follows: ,in For the rotation compensation angle of the fixed device, , , These represent the head's attitude offset angles in the X, Y, and Z axes, respectively. , , These are the compensation coefficients in each axial direction, determined based on the patient's head anatomy and the mechanical characteristics of the fixation device. The correction amount is used to compensate for the effects of mechanical transmission errors and environmental interference factors.

[0012] Furthermore, it also includes: Dynamic control steps for the adjustment speed of the fixation device: The adjustment speed of the fixation device is dynamically adjusted according to the magnitude and rate of change of head posture deviation. The switching threshold of the adjustment speed is determined by analyzing the clinical database, and personalized speed limits are set in combination with the patient's age and physical condition.

[0013] Furthermore, it also includes: Pressure distribution uniformity optimization steps: collect contact pressure data through the array-type pressure sensors built into the fixing device, construct a pressure distribution matrix, simulate the pressure distribution changes under different adjustment parameters using the finite element analysis method, optimize the support structure of the fixing device based on the simulation results, and adjust the material hardness and surface texture of the pad.

[0014] Imaging quality feedback and adjustment steps: Real-time acquisition of image data during the angiography process; analysis of image clarity, contrast, and cerebral vascular display integrity through image quality evaluation indicators; automatic analysis of the cause when the image quality is substandard; if it is caused by head posture deviation, the fixation device is activated for secondary adjustment; if it is caused by uneven pressure distribution, the pressure parameters are optimized; if it is caused by deviation in cerebral vascular orientation tracking, registration and positioning are re-performed.

[0015] Furthermore, it also includes: Data fusion and noise reduction steps: The attitude data collected by the inertial measurement unit and the orientation data collected by the image guidance system are fused. The Kalman filter algorithm is used to remove noise components from the data. During the filtering process, different weight coefficients are set according to the accuracy characteristics of the two types of data. The weight coefficient of the image data is higher than that of the inertial measurement data. At the same time, the filtering parameters are dynamically adjusted to adapt to the data change characteristics at different imaging stages.

[0016] System fault self-diagnosis and emergency handling steps: Real-time monitoring of the operating status of the adjustment mechanism, sensor, and drive module of the fixed device. When a fault is detected, the fault self-diagnosis program is immediately started to locate the fault type and location, and an alarm signal is issued to notify medical staff to handle the situation.

[0017] Compared with existing technologies, the beneficial effects of this invention are: This invention application breaks through the limitations of traditional standardized fixation by constructing a three-dimensional model of the cerebral blood vessel orientation before angiography and a personalized anatomical database of the patient's head. Based on the cerebral blood vessel anatomy features and head contour information extracted from CT or MRI image data, it provides a precise and personalized basis for the initial adjustment of the fixation device, enabling the device to conform to the head shape and spatial orientation of different patients' cerebral blood vessels, thereby improving the adaptability and stability of the fixation from the source and avoiding insecure fixation or patient discomfort caused by improper adaptation.

[0018] During angiography, the application of real-time cerebral vascular orientation tracking and multi-source data fusion technology enables dynamic monitoring and precise compensation of head posture. Through the collaborative work of the image guidance system and the inertial measurement unit, translational and rotational deviations of the head can be quickly captured, driving the multi-degree-of-freedom adjustment mechanism to perform real-time dynamic adjustments. This ensures that cerebral blood vessels are always in the optimal imaging orientation, effectively avoiding imaging blurring and overlap caused by minor head movements. This significantly improves the clarity and integrity of angiography, providing high-quality imaging support for clinical diagnosis.

[0019] Closed-loop control of pressure and posture during the adjustment process further ensures the safety of the examination and patient comfort. Array-type pressure sensors monitor and optimize the uniformity of contact pressure in real time, preventing pain or poor blood circulation caused by excessive local pressure. Simultaneously, by dynamically adjusting the support strength, patient comfort is maximized while maintaining stability, improving examination compliance. A fault self-diagnosis and emergency handling mechanism provides dual protection for examination safety, enabling timely response to device malfunctions and preventing secondary harm to the patient.

[0020] Furthermore, the contrast imaging quality feedback adjustment mechanism forms a closed-loop optimization of "adjustment-imaging-feedback-readjustment," which can dynamically optimize fixed parameters based on imaging effects to ensure that imaging quality meets clinical diagnostic requirements. Multimodal data fusion and noise reduction technology improves the reliability of orientation tracking and attitude monitoring data, providing stable data support for precise adjustment. Preoperative and postoperative data association and storage accumulate valuable experience for subsequent fixation device adjustments for similar patients, helping to continuously optimize clinical diagnosis and treatment.

[0021] Overall, this invention, through core technological innovations such as personalized modeling, real-time tracking, dynamic adjustment, and closed-loop control, comprehensively solves the problems of poor adaptability, low adjustment precision, and lack of dynamic response in traditional head fixation devices. It significantly improves the imaging quality, safety, and patient comfort of cerebral angiography, providing strong support for the accurate diagnosis of cerebrovascular diseases and has significant clinical application value and promotion prospects. Attached Figure Description

[0022] Figure 1 This is a schematic block diagram of the intelligent adjustment method for the head fixation device for angiography based on cerebral blood vessel orientation recognition proposed in this invention. Figure 2 This is a schematic diagram showing the change of head posture deviation over time during cerebral angiography using the head fixation device for angiography based on cerebral blood vessel orientation recognition proposed in this invention. Figure 3 This is a schematic diagram of the contact pressure distribution between the head fixation device and the patient's head in the head fixation device for angiography based on cerebral vascular orientation recognition proposed in this invention. Figure 4 This is a schematic diagram comparing the imaging clarity of the head fixation device for angiography based on cerebral vascular orientation recognition proposed in this invention under different adjustment methods. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0026] Reference Figures 1 to 4 A method for intelligent adjustment of a head fixation device for angiography based on cerebral vascular orientation recognition, comprising the following steps: Step 1: Three-dimensional modeling of cerebral vascular orientation before angiography. The patient's head CT or MRI image data is collected by medical imaging equipment, and the anatomical structural features of the cerebral vascular system, the direction of cerebral vascular branches, the coordinates of key vascular nodes, and the relative positional relationship between blood vessels and skull are extracted. A digital model of cerebral vascular orientation is constructed using a three-dimensional reconstruction algorithm. At the same time, the patient's head contour size and skull landmark position data are collected to establish a personalized anatomical database of the patient's head. Step 2: Initial posture calibration of the fixation device. Install the head fixation device on the contrast examination table. According to the patient's personalized head anatomy database, adjust the height of the support base, the opening angle of the side fixation arm, and the position of the top pressure pad so that the device initially fits the contour of the patient's head. Collect the initial contact pressure data through the pressure sensor built into the device to ensure that the pressure distribution is uniform and within the safe threshold range. Step 3: Real-time cerebral vascular orientation tracking and localization. During the angiography process, the image guidance system dynamically acquires real-time head images, extracts the dynamic coordinate information of key cerebral vascular nodes, registers them with the three-dimensional model constructed before the operation, calculates the head posture offset, translational deviation and rotational deviation, and at the same time, the device's built-in inertial measurement unit acquires head motion posture data to achieve multi-source data fusion tracking of cerebral vascular orientation. Step 4: Intelligent dynamic adjustment of the fixation device. Based on the cerebral blood vessel orientation tracking results and posture offset, the multi-degree-of-freedom adjustment mechanism of the fixation device is driven, including the horizontal translation module, the vertical lifting module, and the rotation adjustment module. The position and angle of the support base, the side fixation arm, and the top pressure pad are adjusted in real time to compensate for head posture offset and keep the cerebral blood vessels in the best imaging position for angiography. Step 5: Adjust the pressure and posture closed-loop control during the process, monitor the pressure distribution data between the fixation device and the head in real time, and automatically adjust the support force of the corresponding part when the pressure exceeds the safety threshold; when the posture deviation exceeds the allowable range, activate the emergency adjustment mechanism to quickly correct the head position, and record the posture data, pressure data and cerebrovascular location data during the adjustment process to form an adjustment log. Step 6: Device reset and data storage after angiography. After the angiography examination is completed, the fixation device gradually loosens the fixation mechanism according to the preset program and slowly resets to the initial state without impacting the patient's head. The preoperative modeling data, intraoperative adjustment data, and angiography imaging quality feedback data are stored together to provide a reference for the adjustment of the fixation device for subsequent similar patients.

[0027] This invention also includes: Steps for accurate fitting of the central axis of cerebral blood vessels: By extracting the coordinates of key feature points in cerebral vascular images, the central axis of cerebral blood vessels is constructed using a least squares fitting algorithm. The fitting formula is as follows: ,in , , , The general parameters for the plane containing the central axis of cerebral blood vessels. , , For the first Three-dimensional coordinates of key cerebrovascular feature points The total number of key feature points is used to determine the spatial orientation of cerebral blood vessels through the fitting results, providing a precise orientation reference for the initial positioning of the fixation device, ensuring that the adjustment direction of the fixation device matches the orientation of cerebral blood vessels, and improving the clarity and accuracy of angiography.

[0028] The calculation steps for personalized head support parameters are as follows: Combining the patient's head contour dimensions, skull landmark locations, and cerebral blood vessel orientation data, the optimal support force and angle parameters for each support part of the fixation device are calculated. The support force adopts a segmented adjustment strategy, setting differentiated pressure thresholds based on the anatomical characteristics of different areas of the head. The pressure thresholds for the frontal, temporal, and occipital regions are dynamically adjusted according to the soft tissue thickness and vascular distribution density of these regions. At the same time, the distribution of support points is optimized based on the relative positions of key cerebral blood vessel nodes and the support points of the fixation device, so that the support points do not directly compress cerebral blood vessels or important nerves, ensuring the stability of the head and the patient's comfort during angiography.

[0029] This invention also includes: Image-guided dynamic registration optimization steps: The iterative nearest-point algorithm is used to optimize the registration between the preoperative 3D model and the intraoperative real-time image. After each registration, the registration error is calculated. When the registration error is greater than the preset threshold, feature points are extracted again and the registration is iterated until the registration error meets the requirements of angiography. During the registration process, the registration accuracy of key cerebrovascular nodes is the focus. The registration priority of key nodes is improved by weighted registration strategy to ensure the accuracy of cerebrovascular orientation tracking and provide a reliable position reference for the dynamic adjustment of the fixation device.

[0030] This invention also includes a head posture deviation compensation algorithm. When a head posture deviation is detected, the algorithm calculates the mapping relationship between the deviation and the adjustment amount to achieve precise compensation adjustment of the fixation device. The compensation formula is as follows: ,in For the rotation compensation angle of the fixed device, , , These represent the head's attitude offset angles in the X, Y, and Z axes, respectively. , , These are the compensation coefficients in each axial direction, determined based on the patient's head anatomy and the mechanical characteristics of the fixation device. To compensate for the correction amount and counteract the effects of mechanical transmission errors and environmental interference factors, this compensation adjustment allows the head posture to quickly return to the optimal imaging orientation, reducing the impact of posture deviation on image quality.

[0031] This invention also includes: Dynamic control steps for the adjustment speed of the fixation device: The adjustment speed of the fixation device is dynamically adjusted according to the magnitude and rate of change of the head posture offset. When the offset is large and the rate of change is fast, a high-speed adjustment mode is used to quickly correct the position. When the offset is small and tends to be stable, the low-speed fine-tuning mode is switched to improve the adjustment accuracy. The switching threshold of the adjustment speed is determined by analyzing a large amount of clinical data. At the same time, personalized speed limits are set in combination with the patient's age and physical condition. The upper limit of the adjustment speed is appropriately reduced for elderly patients or patients with weak physical condition to avoid discomfort caused by excessive adjustment speed.

[0032] This invention also includes: Pressure distribution uniformity optimization steps: Contact pressure data are collected by the array-type pressure sensors built into the fixation device to construct a pressure distribution matrix. The finite element analysis method is used to simulate the pressure distribution changes under different adjustment parameters. Based on the simulation results, the support structure of the fixation device is optimized, and the material hardness and surface texture of the soft pad are adjusted. For areas with concentrated pressure, the pressure is dispersed by increasing the number of support points or using elastic soft pads to make the pressure distribution in the contact area between the head and the device uniform, reducing the risk of patient pain or poor blood circulation caused by excessive local pressure.

[0033] The angiography quality feedback and adjustment process involves real-time acquisition of image data during the angiography process. Image clarity, contrast, and the integrity of cerebral vascular visualization are analyzed using image quality evaluation indicators. When the image quality fails to meet the standards, the cause is automatically analyzed. If it is due to head posture deviation, the fixation device is activated for secondary adjustment. If it is due to uneven pressure distribution, the pressure parameters are optimized. If it is due to deviation in cerebral vascular orientation tracking, registration and positioning are re-performed. This forms a closed-loop optimization mechanism of adjustment-imaging-feedback-readjustment, ensuring that the angiography quality meets clinical diagnostic requirements.

[0034] This invention also includes: Multimodal data fusion and noise reduction steps: The attitude data acquired by the inertial measurement unit and the orientation data acquired by the image guidance system are fused. The Kalman filter algorithm is used to remove noise components from the data to improve the reliability and stability of the data. During the filtering process, different weight coefficients are set according to the accuracy characteristics of the two types of data. The weight coefficient of the image data is higher than that of the inertial measurement data. At the same time, the filtering parameters are dynamically adjusted to adapt to the data change characteristics at different angiography stages, ensuring the continuity and accuracy of cerebral vascular orientation tracking and head posture monitoring.

[0035] System fault self-diagnosis and emergency handling steps: Real-time monitoring of the operating status of the fixing device's adjustment mechanism, sensors, and drive module. When a fault is detected, the fault self-diagnosis program is immediately activated to locate the fault type and location. If it is a minor fault that does not affect safety, it automatically switches to the backup adjustment mode and continues to complete the angiography examination. If it is a serious fault, the adjustment action is immediately stopped, the fixing mechanism is slowly released, and an alarm signal is issued to notify medical staff to handle the situation, ensuring the patient's head safety and avoiding secondary injuries caused by the fault.

[0036] Example 1: Application of cerebral angiography in middle-aged and elderly patients with cerebral aneurysms This embodiment was applied to cerebral angiography of a 62-year-old male patient with a cerebral aneurysm. The patient had a suspected aneurysm in the left middle cerebral artery. Precise head fixation was required to ensure clear imaging of the segment of the blood vessel containing the aneurysm during angiography, assisting the physician in determining the size, shape, and relationship of the lesion to surrounding blood vessels. The patient's head contour was relatively wide, and the soft tissue in the occipital region was relatively thin. Preoperative CT images showed dense branching of cerebral blood vessels, and the aneurysm was located near the bend of the blood vessel, requiring extremely high stability and precision in the fixation posture.

[0037] I. Core Implementation Details Three-dimensional modeling of cerebral vascular orientation before angiography: CT images of the patient's head (1mm slice thickness) were acquired using medical imaging equipment. Image post-processing software was used to extract anatomical features of the cerebral vascular system, including the anterior cerebral artery, middle cerebral artery, posterior cerebral artery, and their branches. The three-dimensional coordinates of 12 key nodes, such as the aneurysm center and vascular bifurcation points, were marked to determine the relative positional relationship between the cerebral vessels and the skull. A surface rendering algorithm was used to construct a digital model of the cerebral vascular orientation, clearly displaying the spatial location of the aneurysm and surrounding vessels. Simultaneously, laser scanning equipment was used to acquire the patient's head contour dimensions and measure the three-dimensional coordinates of eight skull landmarks, including the frontal eminence, parietal eminence, and external occipital protuberance. A personalized anatomical database of the patient's head was established, containing three-dimensional head contour data, cerebral vascular model data, and landmark coordinate data, providing a basis for subsequent adjustments.

[0038] Initial posture calibration of the fixation device: The head fixation device is installed in the preset position on the contrast examination table. The device includes a support base, two adjustable fixation arms, a top arc-shaped pressure pad, and a multi-degree-of-freedom adjustment mechanism. Based on the patient's personalized head anatomy database, key parameters are input through the device control panel to drive the support base to rise and fall to a height of 25cm above the examination table surface, ensuring that the external auditory canal is aligned with the imaging center of the contrast equipment after the patient's head is placed. The opening angle of the two fixation arms is adjusted to 65° to conform to the patient's temporal contour. The top pressure pad is moved to 3cm above the occipital region. After initial fixation, the pressure detection program is initiated. The device's built-in 16-channel array pressure sensors are evenly distributed on the inner side of the fixation arms and the surface of the pressure pad to collect initial contact pressure data. The pressure output of the fixation arms and the pad is adjusted to control the contact pressure of the forehead, temporal region, and occipital region at 25kPa, 20kPa, and 18kPa, respectively, with uniform pressure distribution within the safe threshold range of 15-30kPa.

[0039] Real-time cerebral vascular orientation tracking and localization: During angiography, the image-guided system acquires one frame of real-time head image every 0.5 seconds. Dynamic coordinate information of key cerebral vascular nodes is extracted using a feature point matching algorithm and registered with the pre-constructed 3D model. An iterative nearest-point algorithm is used to optimize the registration effect. After each registration, the registration error is calculated. When the error exceeds 0.3 mm, feature points are re-extracted and iterated until the registration error is less than 0.3 mm. Simultaneously, the built-in inertial measurement unit acquires angular velocity and acceleration data of the head in the X, Y, and Z axes, outputting head motion posture data every 0.1 seconds. The image data and inertial measurement data are fused using a Kalman filter algorithm to remove minor noise caused by respiration and heartbeat, achieving continuous tracking of cerebral vascular orientation and real-time output of head translational and rotational deviations.

[0040] Intelligent dynamic adjustment of the fixation device: Based on the fused cerebral vascular orientation tracking results, when a translational deviation of 0.5mm or a rotational deviation of 0.2° is detected in the head, the system drives the multi-degree-of-freedom adjustment mechanism to initiate dynamic adjustment. The horizontal translation module drives the support base to make fine adjustments along the X and Y axes via a stepper motor to compensate for translational deviations; the vertical lifting module finely adjusts the height of the support base to ensure that the cerebral blood vessels are always in the imaging center; the rotation adjustment module drives the fixation arm and the pressure pad to rotate in coordination to correct rotational deviations. For example, when the patient's head rotates to the right by 0.3° due to neck fatigue, the system immediately drives the left fixation arm to make a 5mm forward adjustment, the right fixation arm to make a 3mm backward adjustment, and the left side of the top pressure pad to make a 2mm downward adjustment, while the right side remains stationary, quickly compensating for rotational deviations and ensuring that the segment of the aneurysm is always in the optimal imaging position for angiography.

[0041] Closed-loop control of pressure and posture during adjustment: During adjustment, an array of pressure sensors monitors the contact pressure distribution in real time, updating the pressure data every 0.2 seconds to construct a pressure distribution matrix. When the pressure in the right temporal region rises to 32 kPa, exceeding the safety threshold, the system automatically reduces the pressure output of the right fixation arm, restoring the pressure to 23 kPa. When the pressure in the occipital region drops below 15 kPa, the support force of the top pressure pad is appropriately increased to ensure fixation stability. Simultaneously, the posture monitoring system continuously tracks head displacement. When the displacement exceeds the allowable range of 0.8 mm or 0.5°, an emergency adjustment mechanism is activated, and the adjustment mechanism corrects the head position at twice the speed to avoid imaging distortion. During adjustment, the system automatically records posture data, pressure data, and cerebral vascular orientation data, generating an adjustment log every minute. The log includes adjustment time, displacement, adjustment parameters, and pressure changes.

[0042] Post-angiography device reset and data storage: After the angiography examination, the fixation device gradually loosens according to a preset procedure. First, the pressure of the top compression pad is reduced to 5 kPa, then the two fixation arms are slowly opened to 90°, and finally the support base is lowered to its initial height. The entire reset process takes 30 seconds to avoid impact on the patient's head. The system associates and stores preoperative modeling data, intraoperative adjustment data, and angiography imaging quality feedback data in the hospital's image archiving system. The data is categorized and managed by patient ID, providing a reference for subsequent fixation device adjustments for similar patients. Simultaneously, the device automatically performs self-tests to ensure that all modules are operating normally, preparing for the next use.

[0043] Table 1 shows a comparison of angiographic examination results in middle-aged and elderly patients with cerebral aneurysms: Table 1

[0044] Table 1 clearly demonstrates the advantages of this application in angiography for middle-aged and elderly patients with cerebral aneurysms. Traditional fixation methods rely on manual adjustment, which is difficult to adapt to the individual head characteristics of patients, resulting in generally poor image clarity, insufficient display of lesion details, and the need for three interruptions during the procedure for posture correction, with a total duration of up to 45 minutes. Due to uneven distribution of fixation pressure, patients experience significant temporal pain after the examination, leading to poor comfort. This application achieves a high degree of fit between the device and the patient's head through personalized 3D modeling and precise initial calibration. The real-time tracking and dynamic adjustment mechanism effectively counteracts minor head deviations, eliminating the need for manual correction during the procedure and shortening the examination time to 32 minutes. Closed-loop pressure control ensures uniform and moderate contact pressure, resulting in no significant discomfort for patients after the examination. Angiographic imaging can clearly display the size, shape, and relationship of the aneurysm with surrounding vessels, providing high-quality imaging support for clinical diagnosis and fully demonstrating the precision and practicality of the intelligent adjustment method.

[0045] Example 2: Application of cerebral angiography in adolescent patients with cerebral vascular malformations This embodiment was applied to angiography of a 17-year-old adolescent with cerebral vascular malformation. The patient had a cerebral vascular malformation in the right parietal lobe of the brain. Angiography was needed to clarify the extent of the malformed vascular cluster and the location of the feeding arteries and draining veins to provide a basis for subsequent interventional treatment planning. The patient was in the growth and development stage, with a narrow head contour and incompletely defined skull landmarks. The cerebral vascular malformation caused disordered local vascular pathways. Preoperative MRI images showed that the malformed vascular cluster had a rich blood supply, and the patient had a low pain tolerance, making it easy for the head to move due to discomfort during the examination. Therefore, the fit, comfort, and dynamic adjustment response speed of the fixation device were strictly required.

[0046] I. Core Implementation Details Three-dimensional modeling of cerebral vascular orientation before angiography: MRI images of the patient's head (1mm slice thickness) were acquired using medical imaging equipment. Volumetric reconstruction algorithms were used to extract anatomical features of the cerebral vascular system, highlighting 15 key nodes, including the boundary points of the malformed vascular cluster, the origin of the feeding artery, and the end of the draining vein. The relative positional relationship between the malformed and normal cerebral blood vessels was clarified, and a digital model of cerebral vascular orientation was constructed, visually representing the spatial distribution of the malformed vascular cluster. Laser scanning equipment was used to acquire the patient's head contour dimensions and measure the three-dimensional coordinates of six skull landmarks. Considering the developmental characteristics of adolescent heads, additional basic information such as patient age and head circumference was recorded in the database, establishing a personalized anatomical database for the patient's head, providing data support for personalized adjustments.

[0047] Initial posture calibration of the fixation device: The head fixation device is installed on the angiography examination table. Based on the patient's personalized head anatomy database, the support base is raised and lowered to a height of 22cm above the examination table surface, ensuring the patient's line of sight is horizontal after the head is positioned. The opening angle of the two fixation arms is adjusted to 55° to conform to the narrow contour of the patient's temporal region. The top pressure pad is moved to 2cm above the occipital region. A soft silicone pad with a hardness of Shore A30 is selected to reduce pressure on the adolescent's head. The pressure detection program is activated, and the 16-channel array pressure sensor collects initial contact pressure data. The pressure output of the fixation arms and the pad is adjusted to control the contact pressure of the forehead, temporal region, and occipital region at 20kPa, 18kPa, and 15kPa, respectively. The pressure distribution is uniform and within the safe threshold range of 12-28kPa, suitable for the tolerance of adolescents. Simultaneously, the initial angle of the fixation device is fine-tuned according to the location of the cerebral vascular malformation, ensuring that the area of ​​the malformed vascular cluster is in the central field of view of the angiography imaging.

[0048] Real-time cerebral vascular orientation tracking and localization: During angiography, the image-guided system acquires a real-time head image every 0.3 seconds. Dynamic coordinate information of key cerebral vascular nodes is extracted using a feature point matching algorithm and registered with the preoperative 3D model. A weighted registration strategy is employed to prioritize the registration of malformed vascular cluster boundaries with the origin of the feeding artery, keeping the registration error within 0.2 mm. The built-in inertial measurement unit outputs head motion posture data every 0.08 seconds. A Kalman filter algorithm is used to fuse and denoise the image data and inertial measurement data, eliminating noise components and achieving high-precision continuous tracking of cerebral vascular orientation, outputting real-time translational and rotational head deviations. Considering the high mobility of adolescents, the posture monitoring sensitivity is set to 0.3 mm translational deviation or 0.15° rotational deviation to ensure timely capture of minute posture changes.

[0049] Intelligent dynamic adjustment of the fixation device: Based on the fused tracking results, when a 0.3mm translational deviation or a 0.15° rotational deviation of the head is detected, the system drives the multi-degree-of-freedom adjustment mechanism to respond quickly. The horizontal translation module, the vertical lifting module, and the rotation adjustment module work together to precisely adjust the position and angle of the support base, fixation arms, and compression pads according to the type and magnitude of the deviation. For example, when the patient's head shifts forward by 0.4mm due to swallowing, the system immediately drives the support base to adjust backward by 0.4mm, while maintaining the pressure of the fixation arms and compression pads on both sides, quickly compensating for the translational deviation and ensuring that the malformed vascular cluster is always in the optimal imaging position. The adjustment speed is dynamically controlled according to the deviation. When the deviation is small, a low-speed fine-tuning mode is used with an adjustment speed of 0.5mm / s. When the deviation is large, it switches to a high-speed adjustment mode with an adjustment speed of 1.0mm / s, ensuring both adjustment accuracy and meeting the response speed requirements.

[0050] Closed-loop control of pressure and posture during adjustment: During adjustment, an array of pressure sensors monitors the contact pressure distribution in real time, updating the pressure data every 0.15 seconds. Finite element analysis is used to simulate pressure distribution changes, ensuring uniform pressure in the head contact area. When the pressure in the left temporal region rises to 29 kPa, exceeding the safety threshold, the system automatically reduces the pressure output of the left fixation arm, restoring the pressure to 20 kPa. Considering the thinner soft tissue in the occipital region of adolescents, the system focuses on monitoring occipital pressure to avoid discomfort caused by excessive pressure. When the posture deviation exceeds the allowable range of 0.6 mm or 0.3°, an emergency adjustment mechanism is activated to quickly correct the head position. During adjustment, the system automatically records relevant data, generates an adjustment log, and simultaneously acquires angiographic image data in real time. Image quality evaluation indicators are used to analyze imaging clarity and vascular visualization integrity.

[0051] Post-angiography device reset and data storage: After the angiography examination, the fixation device is gradually released according to a preset procedure. First, the pressure of the top compression pad is reduced to 3 kPa, then the two fixation arms are slowly opened to 85°, and finally the support base is lowered to its initial height. The reset process takes 40 seconds, and the movements are gentle to avoid impact on the patient's head. The system associates and stores preoperative modeling data, intraoperative adjustment data, and imaging quality feedback data to provide a reference for subsequent fixation adjustments for similar adolescent patients. After the device completes self-testing to ensure that all modules are operating normally, it awaits the next use.

[0052] Table 2 shows a comparison of angiography results in adolescent patients with cerebral vascular malformations: Table 2

[0053] Table 2 data highlights the application value of this application in angiography for adolescent patients with cerebrovascular malformations. Traditional fixation methods, with their standardized design, cannot adapt to the narrow head contours of adolescents, resulting in poor image clarity of the malformed vessels. Patients experienced head movement four times due to pressure discomfort, requiring interruptions for correction, with a total examination time of 40 minutes and generally low physician satisfaction. This application, through personalized 3D modeling and precise initial calibration, achieves a high degree of fit between the device and the adolescent's head. The application of array-type pressure sensors and soft pads improves patient comfort, with no head movement during the procedure. The real-time tracking and dynamic adjustment mechanism responds quickly to minute posture changes, ensuring rapid adjustment and clear imaging of the malformed vascular cluster. The total examination time is reduced to 28 minutes, allowing physicians to clearly observe the extent of the malformed vascular cluster, feeding arteries, and draining veins, resulting in high diagnostic satisfaction. This application's method fully considers the physiological characteristics and examination needs of adolescents, solving the problems of poor adaptability, patient discomfort, and poor image quality associated with traditional fixation methods, providing a reliable fixation solution for angiography of cerebrovascular diseases in adolescents.

[0054] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent adjustment of a head fixation device for angiography based on cerebral vascular orientation recognition, characterized in that, include: Three-dimensional modeling of cerebral vascular orientation before angiography: CT or MRI images of the patient's head are acquired using medical imaging equipment, and the anatomical features of the cerebral vascular system, the direction of cerebral vascular branches, the coordinates of vascular nodes, and the relative positional relationship between the blood vessels and the skull are extracted. A digital model of cerebral vascular orientation is constructed using a three-dimensional reconstruction algorithm. At the same time, the patient's head contour dimensions and skull landmark location data are acquired to establish a patient head anatomy database. Initial posture calibration of the fixation device: The head fixation device is installed on the contrast examination table. Based on the patient's personalized head anatomy database, the height of the support base, the opening angle of the side fixation arm, and the position of the top pressure pad are adjusted to make the device initially fit the contour of the patient's head. The initial contact pressure data is collected through the pressure sensor built into the device to make the pressure distribution uniform and within the safe threshold range. Real-time cerebrovascular orientation tracking and localization: During the angiography process, the image guidance system dynamically acquires real-time head images, extracts the dynamic coordinate information of cerebrovascular nodes, registers them with the three-dimensional model constructed before the operation, calculates the head posture offset, translational deviation and rotational deviation, and at the same time, the device's built-in inertial measurement unit acquires head motion posture data to achieve data fusion tracking of cerebrovascular orientation. Intelligent dynamic adjustment of the fixation device: Based on the cerebral blood vessel orientation tracking results and posture offset, the fixation device's degree of freedom adjustment mechanism, including the horizontal translation module, vertical lifting module, and rotation adjustment module, adjusts the position and angle of the support base, side fixation arm, and top pressure pad in real time to compensate for head posture offset, so that the cerebral blood vessels are always in the best imaging position for angiography. Pressure and posture closed-loop control during adjustment: Real-time monitoring of pressure distribution data between the fixation device and the head; when the pressure exceeds the safety threshold, the support strength of the corresponding part is automatically adjusted; when the posture deviation exceeds the allowable range, the emergency adjustment mechanism is activated to quickly correct the head position, and posture data, pressure data and cerebrovascular location data are recorded during the adjustment process to form an adjustment log. After contrast imaging, the device is reset and data is stored. After the angiography is completed, the fixation device gradually loosens the fixation mechanism according to the preset program and slowly returns to the initial state without causing impact to the patient's head. The preoperative modeling data, intraoperative adjustment data, and angiography imaging quality feedback data are stored together.

2. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: Steps for fitting the central axis of cerebral blood vessels: By extracting the coordinates of feature points in cerebral vascular images, the central axis of cerebral blood vessels is constructed using a least-squares fitting algorithm. The fitting formula is as follows: ,in , , , The general parameters for the plane containing the central axis of cerebral blood vessels. , , For the first Three-dimensional coordinates of cerebral vascular feature points The total number of feature points is used to determine the spatial orientation of cerebral blood vessels through fitting results, providing an orientation reference for the initial positioning of the fixation device.

3. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: The steps for calculating personalized head support parameters for patients are as follows: Combining the patient's head contour dimensions, skull landmark locations, and cerebral blood vessel orientation data, the optimal support force and angle parameters for each support part of the fixation device are calculated. The support force adopts a segmented adjustment strategy, setting differentiated pressure thresholds for different anatomical structures in different areas of the head. At the same time, the distribution of support points is optimized based on the relative positions of cerebral blood vessel nodes and fixation device support points.

4. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: Image-guided dynamic registration optimization steps: The iterative nearest point algorithm is used to optimize the registration between the preoperative 3D model and the intraoperative real-time image. After each registration, the registration error is calculated. When the registration error is greater than the preset threshold, feature points are extracted again for registration iteration until the registration error meets the requirements of angiography. The registration priority of nodes is improved by weighted registration strategy.

5. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, It also includes a head posture deviation compensation algorithm. When a head posture deviation is detected, the algorithm calculates the mapping relationship between the deviation and the adjustment amount to achieve compensation adjustment of the fixation device. The compensation formula is as follows: ,in For the rotation compensation angle of the fixed device, , , These represent the head's attitude offset angles in the X, Y, and Z axes, respectively. , , These are the compensation coefficients in each axial direction, determined based on the patient's head anatomy and the mechanical characteristics of the fixation device. The correction amount is used to compensate for the effects of mechanical transmission errors and environmental interference factors.

6. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: The dynamic control steps for the adjustment speed of the fixation device are as follows: The adjustment speed of the fixation device is dynamically adjusted according to the magnitude and rate of change of the head posture offset. The switching threshold of the adjustment speed is determined by analyzing the clinical database, and personalized speed limits are set in combination with the patient's age and physical condition.

7. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: Pressure distribution uniformity optimization steps: collect contact pressure data through the array-type pressure sensors built into the fixing device, construct a pressure distribution matrix, use the finite element analysis method to simulate the pressure distribution changes under different adjustment parameters, optimize the support structure of the fixing device based on the simulation results, and adjust the material hardness and surface texture of the pad.

8. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: Imaging quality feedback and adjustment steps: Real-time acquisition of image data during the angiography process; analysis of image clarity, contrast, and cerebral vascular display integrity through image quality evaluation indicators; automatic analysis of the cause when the image quality is substandard; if it is caused by head posture deviation, the fixation device is activated for secondary adjustment; if it is caused by uneven pressure distribution, the pressure parameters are optimized; if it is caused by deviation in cerebral vascular orientation tracking, registration and positioning are re-performed.

9. The intelligent adjustment method for the head fixation device for angiography based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: Data fusion and noise reduction steps: The attitude data collected by the inertial measurement unit and the orientation data collected by the image guidance system are fused. The Kalman filter algorithm is used to remove noise components from the data. During the filtering process, different weight coefficients are set according to the accuracy characteristics of the two types of data. The weight coefficient of the image data is higher than that of the inertial measurement data. At the same time, the filtering parameters are dynamically adjusted to adapt to the data change characteristics at different imaging stages.

10. The intelligent adjustment method for angiography head fixation device based on cerebral vascular orientation recognition according to claim 1, characterized in that, Also includes: System fault self-diagnosis and emergency handling steps: Real-time monitoring of the operating status of the adjustment mechanism, sensor, and drive module of the fixed device. When a fault is detected, the fault self-diagnosis program is immediately started to locate the fault type and location, and an alarm signal is issued to notify medical staff to handle the situation.

Images