Preparation of cerebral venous sinus stent and implant navigation positioning method and system

Abstract

The application provides a preparation and implant navigation positioning method and system of a cerebral venous sinus stent, relates to the technical field of stent implantation, and comprises the following steps: determining a geometric center line of a venous sinus stenosis section and a stent release position based on preoperative images; and mapping a planned path to a real-time coordinate system through intraoperative image registration. In the process of catheter delivery, the deviation of the catheter tip position from the planned path is monitored in real time, and correction instructions are generated to guide the catheter to accurately reach the predetermined position to release the stent. The method realizes real-time navigation and accurate positioning of the stent implantation process, and improves the safety and effectiveness of the operation.

Description

Technical Field

[0001] This invention relates to the field of stent implantation technology, and in particular to the preparation of cerebral venous sinus stents and the navigation and positioning methods and systems for implantation. Background Technology

[0002] Cerebral venous sinus stenosis is a significant cause of increased intracranial pressure and related neurological disorders. Currently, interventional treatment for this type of stenosis primarily relies on the implantation of metallic stents to restore patency of the venous sinuses. In existing practice, the routine stent implantation procedure is typically planned based on preoperative angiographic images. By analyzing these static two-dimensional or three-dimensional images, physicians roughly assess the location, length, and morphology of the stenosis and plan the stent implantation path and expected release location accordingly. During the procedure, the physician relies mainly on real-time two-dimensional images under X-ray fluoroscopy for manual manipulation. Using their experience, they advance the stent-carrying microcatheter to the target area and release the stent under visual guidance.

[0003] There is an unavoidable discrepancy between preoperative planning based on static images and the dynamic vascular anatomy during surgery. Patient breathing, heartbeat, and minor changes in body position during surgery can all cause the actual spatial location of the blood vessels to not perfectly match the preoperative model, making the pre-planned implantation path and release location less precise during the procedure. Relying solely on the surgeon's spatial imagination and operational experience for three-dimensional spatial navigation based on two-dimensional images is prone to errors in judgment, leading to inaccurate stent positioning, failure to completely cover the stenotic segment, or unsatisfactory release locations. Current methods lack real-time, quantitative guidance and correction mechanisms for the delivery process. As the catheter travels within the tortuous venous sinuses, the deviation between its tip position and the pre-set ideal path is dynamically changing. Conventional methods struggle to detect this deviation promptly and accurately and provide specific correction guidance, relying more on the surgeon's feel and repeated angiography verification. This not only prolongs the operation time and increases contrast agent usage and radiation exposure but also potentially increases the risks of the procedure. Summary of the Invention

[0004] The present invention provides a method and system for the preparation and implantation of cerebral venous sinus stents, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides a method for preparing and implanting a cerebral venous sinus stent using navigation and positioning, comprising: Preoperative and intraoperative real-time imaging data of the patient's cerebral venous sinus are acquired. The pixel set of the lumen region is determined based on the preoperative imaging data, and the geometric center line of the stenotic segment of the venous sinus is identified in the pixel set of the lumen region. The stent implantation path is calculated according to the spatial orientation of the geometric center line, and the stent release position is determined based on the lumen diameter change rate of the stenotic segment. The stent release position corresponds to a preset distance in front of the proximal end of the vascular segment with the largest lumen diameter change rate. The intraoperative real-time image data is registered with the pixel set of the lumen region. The registration transformation matrix is ​​calculated by extracting the spatial positional deviation between the vessel wall boundary feature points in the intraoperative real-time image data and the corresponding feature points in the pixel set of the lumen region. The registration transformation matrix is ​​applied to map the stent implantation path and the stent release position to the coordinate system of the intraoperative real-time image data. During the stent delivery process, the spatial coordinates of the catheter tip are collected in real time. The deviation between the spatial coordinates and the stent implantation path is calculated. When the deviation exceeds the safety threshold, a path correction command is generated. The catheter pushing direction is adjusted according to the path correction command until the catheter tip reaches the stent release position, and the stent is released at the stent release position.

[0006] Based on the preoperative imaging data, a pixel set of the lumen region is determined, and the geometric centerline of the venous sinus stenosis segment is identified within this pixel set. The stent implantation path is calculated based on the spatial orientation of the geometric centerline, and the stent release position is determined based on the rate of change of the lumen diameter of the stenosis segment, including: The preoperative image data is processed using a gray-level gradient-based segmentation algorithm to extract the luminal boundary of the venous sinus. The luminal boundary is sliced ​​layer by layer along the blood flow direction. The pixel set of the luminal region is extracted in each slice layer, and the centroid coordinates of the pixel set are calculated as the center point coordinates of that layer. The center point coordinates of each slice layer are connected sequentially according to the blood flow direction to form an initial center line. The geometric center line of the venous sinus stenosis segment is obtained by fitting the initial center line with a cubic spline curve. Multiple sampling points are set at fixed intervals along the geometric center line, and the cross-sectional diameter of the lumen perpendicular to the geometric center line is calculated at each sampling point to obtain the lumen diameter distribution sequence; The derivative of the lumen diameter distribution sequence is calculated to obtain the lumen diameter change rate sequence, and the position with the largest absolute value of the change rate in the lumen diameter change rate sequence is identified as the most severe stenosis region. Using the most severely stenotic area as a reference, the stent release position is determined by offsetting a preset distance in the proximal direction. Starting from the stent release position, the stent is extended along the geometric center line to the normal blood vessel area at the distal end of the stenotic segment, and this extension path is determined as the stent implantation path.

[0007] The preoperative image data was processed using a gray-level gradient-based segmentation algorithm to extract the luminal boundary of the venous sinus. The luminal boundary was then sliced ​​layer by layer along the blood flow direction. In each slice layer, a set of pixels representing the luminal region was extracted, including: The preoperative image data is standardized for grayscale values, and the grayscale difference value of each voxel point in three orthogonal directions is calculated. The grayscale difference values ​​in the three orthogonal directions are combined to form the grayscale gradient vector of the voxel point. The magnitude of the gray-level gradient vector is calculated as the gradient magnitude. Voxel points with gradient magnitudes greater than a preset gradient threshold are marked as boundary candidate points. The positions of the boundary candidate points where the gradient direction change angle is greater than the angle threshold are determined as the inflection points of the blood vessel wall boundary. A closed lumen boundary is constructed based on the inflection points of the blood vessel wall boundary. Obtain blood flow direction information of the venous sinus, establish a slice coordinate system along the blood flow direction, wherein the vertical axis of the slice coordinate system is parallel to the blood flow direction, and the horizontal plane is perpendicular to the blood flow direction; In the slice coordinate system, the cavity boundary is sliced ​​and sampled according to a fixed layer spacing. All voxel points intersecting the cavity boundary are extracted in the transverse plane of each slice layer. The coordinates of the voxel points are projected onto the two-dimensional plane of the slice layer. Boundary tracing is performed on the projected voxel coordinates to identify the voxel sequence that constitutes the closed contour, and all voxel points inside the closed contour are determined as the pixel set of the lumen region of the slice layer.

[0008] The intraoperative real-time image data is registered with the pixel set of the lumen region. The registration transformation matrix is ​​calculated by extracting the spatial positional deviation between the vessel wall boundary feature points in the intraoperative real-time image data and the corresponding feature points in the pixel set of the lumen region, including: Determine the gray-level gradient vector of each pixel in the intraoperative real-time image data, and identify pixels whose gray-level gradient amplitude exceeds the boundary threshold as candidate points of the intraoperative vessel wall boundary; perform connected component analysis on the candidate points of the intraoperative vessel wall boundary, extract the point with the largest rate of curvature change on the boundary of the connected component as the feature point of the intraoperative vessel wall boundary, and record the spatial coordinates and local tangent direction of each feature point of the intraoperative vessel wall boundary. In the pixel set of the lumen region, identify the inflection point positions on the lumen contour of each slice layer, calculate the curvature value at the inflection point position, and determine the inflection point with a curvature value greater than the curvature threshold as the lumen boundary feature point; Establish an initial correspondence between the intraoperative vessel wall boundary feature points and the lumen boundary feature points. For each intraoperative vessel wall boundary feature point, search for the feature point in the lumen boundary feature point set that has the closest geometric position and the smallest local tangent direction angle as the corresponding point. Calculate the spatial position deviation between each pair of corresponding points, and decompose the spatial position deviation into translation components along the coordinate axis and rotation components around the coordinate axis; calculate the global translation vector by weighted averaging of the translation components of all corresponding point pairs, and calculate the global rotation parameters by weighted averaging of the rotation components of all corresponding point pairs, wherein the weighting coefficients are determined based on the matching confidence between corresponding point pairs. A translation transformation matrix is ​​constructed based on the global translation vector, and a rotation transformation matrix is ​​constructed based on the global rotation parameters. The translation transformation matrix and the rotation transformation matrix are then multiplied to obtain the registration transformation matrix.

[0009] The registration transformation matrix is ​​used to map the stent implantation path and the stent release position to the coordinate system of the intraoperative real-time image data, including: Extract the spatial coordinates of each sampling point on the stent implantation path, organize the spatial coordinates of each sampling point into a coordinate matrix, apply the registration transformation matrix to the coordinate matrix to perform matrix operations, and obtain the spatial position of the mapped stent implantation path in the intraoperative real-time image data coordinate system. The coordinate transformation matrix is ​​applied to the coordinates of the stent release position to obtain the corresponding coordinates of the stent release position in the intraoperative real-time image data coordinate system, and the corresponding coordinates are marked as the intraoperative target release point.

[0010] During stent delivery, the spatial coordinates of the catheter tip are acquired in real time. The deviation between these spatial coordinates and the stent implantation path is calculated. When the deviation exceeds a safety threshold, a path correction command is generated. The catheter delivery direction is adjusted according to the path correction command, including: The position information of the catheter tip is acquired in real time by intraoperative imaging equipment, and the position information is converted into spatial coordinates in the intraoperative real-time imaging data coordinate system; Calculate the shortest distance from the spatial coordinates to the stent implantation path, determine the projection point on the stent implantation path that is closest to the spatial coordinates, and define the shortest distance as the lateral deviation value; Calculate the difference between the projected length of the spatial coordinates along the tangent direction of the stent implantation path and the arc length position of the projection point on the stent implantation path, and define the difference as the longitudinal deviation value; The horizontal deviation value and the vertical deviation value are vectorized to obtain a comprehensive deviation value, and it is determined whether the comprehensive deviation value exceeds a preset safety threshold. When the overall deviation value exceeds the safety threshold, a deviation correction vector is calculated from the spatial coordinates to the projection point, and the spatial direction that the catheter tip needs to be adjusted is determined based on the deviation correction vector. The angle that needs to be changed for the catheter pushing operation is calculated based on the spatial direction. The angle includes the horizontal deflection angle and the vertical deflection angle relative to the current pushing direction.

[0011] A second aspect of the present invention provides a navigation and positioning system for the preparation and implantation of a cerebral venous sinus stent, comprising: The image acquisition unit is used to acquire preoperative image data and intraoperative real-time image data of the patient's cerebral venous sinus. Based on the preoperative image data, it determines the pixel set of the lumen region and identifies the geometric center line of the stenotic segment of the venous sinus in the pixel set of the lumen region. It calculates the stent implantation path according to the spatial orientation of the geometric center line and determines the stent release position based on the lumen diameter change rate of the stenotic segment. The stent release position corresponds to a preset distance in front of the proximal end of the vascular segment with the largest lumen diameter change rate. The registration transformation unit is used to register the intraoperative real-time image data with the pixel set of the lumen region. It calculates the registration transformation matrix by extracting the spatial position deviation between the feature points of the vessel wall boundary in the intraoperative real-time image data and the corresponding feature points in the pixel set of the lumen region. The stent delivery unit is used to apply the registration transformation matrix to map the stent implantation path and the stent release position to the coordinate system of the intraoperative real-time image data. During the stent delivery process, the spatial coordinates of the catheter tip are acquired in real time, the deviation between the spatial coordinates and the stent implantation path is calculated, and when the deviation exceeds a safety threshold, a path correction command is generated. The catheter pushing direction is adjusted according to the path correction command until the catheter tip reaches the stent release position, and the stent is released at the stent release position.

[0012] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0014] The beneficial effects of this application are as follows: This method enables precise positioning and navigation for stent implantation in cerebral venous sinus stenosis. Based on preoperative imaging data, it extracts pixel sets from the lumen region and identifies the geometric centerline of the stenosis. The stent implantation path is then calculated to ensure a high degree of conformity between the planned path and the vascular anatomy, avoiding the risk of vascular damage due to path deviation. The stent release position is determined based on the rate of change of the stenotic lumen diameter, setting the release point at a predetermined distance anterior to the proximal end of the segment with the largest rate of diameter change. This ensures the stent accurately covers the most significant stenosis area, optimizing the stent's support effect on the vessel wall while preventing stent displacement or over-expansion.

[0015] The registration process between intraoperative real-time image data and the preoperative pixel set of the luminal region involves extracting feature points of the vessel wall boundary and calculating spatial positional deviations to obtain a high-precision registration transformation matrix. This registration operation effectively integrates preoperative planning information with real-time intraoperative anatomical structures, overcoming positioning errors caused by changes in patient position or tissue deformation, and providing a reliable coordinate mapping basis for subsequent navigation.

[0016] A registration transformation matrix was applied to map the stent implantation path and release position to the intraoperative real-time coordinate system, achieving a seamless transition from preoperative planning to the intraoperative environment. During catheter delivery, the spatial coordinates of the catheter tip were acquired in real time and the deviation from the planned path was calculated. When the deviation exceeded a safety threshold, a path correction command was automatically generated. This real-time feedback mechanism allows the operator to adjust the catheter push direction in a timely manner, ensuring that the catheter travels accurately along the predetermined path and significantly reducing the risk of mechanical stimulation or perforation of the vascular intima during the operation. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of the preparation and implantation navigation and positioning method of the cerebral venous sinus stent according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the distribution parameters of the initial matching points for pipeline defects according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0019] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0020] Figure 1 This is a schematic flowchart illustrating the preparation and implantation navigation and positioning method of the cerebral venous sinus stent according to an embodiment of the present invention. Figure 1 As shown, the method includes: Preoperative and intraoperative real-time imaging data of the patient's cerebral venous sinus are acquired. The pixel set of the lumen region is determined based on the preoperative imaging data, and the geometric center line of the stenotic segment of the venous sinus is identified in the pixel set of the lumen region. The stent implantation path is calculated according to the spatial orientation of the geometric center line, and the stent release position is determined based on the lumen diameter change rate of the stenotic segment. The stent release position corresponds to a preset distance in front of the proximal end of the vascular segment with the largest lumen diameter change rate. The intraoperative real-time image data is registered with the pixel set of the lumen region. The registration transformation matrix is ​​calculated by extracting the spatial positional deviation between the vessel wall boundary feature points in the intraoperative real-time image data and the corresponding feature points in the pixel set of the lumen region. The registration transformation matrix is ​​applied to map the stent implantation path and the stent release position to the coordinate system of the intraoperative real-time image data. During the stent delivery process, the spatial coordinates of the catheter tip are collected in real time. The deviation between the spatial coordinates and the stent implantation path is calculated. When the deviation exceeds the safety threshold, a path correction command is generated. The catheter pushing direction is adjusted according to the path correction command until the catheter tip reaches the stent release position, and the stent is released at the stent release position.

[0021] In one optional implementation, a pixel set of the lumen region is determined based on the preoperative imaging data, and the geometric centerline of the venous sinus stenosis segment is identified in the pixel set of the lumen region. The stent implantation path is calculated based on the spatial orientation of the geometric centerline, and the stent release position is determined based on the rate of change of the lumen diameter of the stenosis segment, including: The preoperative image data is processed using a gray-level gradient-based segmentation algorithm to extract the luminal boundary of the venous sinus. The luminal boundary is sliced ​​layer by layer along the blood flow direction. The pixel set of the luminal region is extracted in each slice layer, and the centroid coordinates of the pixel set are calculated as the center point coordinates of that layer. The center point coordinates of each slice layer are connected sequentially according to the blood flow direction to form an initial center line. The geometric center line of the venous sinus stenosis segment is obtained by fitting the initial center line with a cubic spline curve. Multiple sampling points are set at fixed intervals along the geometric center line, and the cross-sectional diameter of the lumen perpendicular to the geometric center line is calculated at each sampling point to obtain the lumen diameter distribution sequence; The derivative of the lumen diameter distribution sequence is calculated to obtain the lumen diameter change rate sequence, and the position with the largest absolute value of the change rate in the lumen diameter change rate sequence is identified as the most severe narrowing region. Using the most severely stenotic area as a reference, the stent release position is determined by offsetting a preset distance in the proximal direction. Starting from the stent release position, the stent is extended along the geometric center line to the normal blood vessel area at the distal end of the stenotic segment, and this extension path is determined as the stent implantation path.

[0022] For preoperative venous sinus imaging data, the first step is to accurately extract the luminal boundaries of the venous sinuses to determine the analysis area. A gray-level gradient-based segmentation algorithm is used to calculate the spatial gradient of gray values ​​at each voxel location in the image data. Locations with drastic gradient changes typically correspond to tissue interfaces. The density difference between the venous sinus lumen and the surrounding brain tissue manifests as a significant gray-level difference in CT or MRI images. By calculating the gradient amplitude and setting a threshold, the pixel location of the luminal boundary can be identified. To improve the accuracy of boundary extraction, Gaussian smoothing filtering is applied to the image data before gradient calculation to reduce noise interference. The filter kernel size is adjusted according to the spatial resolution of the image, typically choosing a 3×3×3 or 5×5×5 kernel. After boundary extraction, morphological processing is required to remove isolated noise points and small connected regions, ensuring a complete and continuous luminal boundary.

[0023] After determining the luminal boundaries, the three-dimensional luminal region is sliced ​​according to the blood flow direction of the venous sinuses. The blood flow direction is usually determined based on the anatomical orientation of the venous sinuses; for the superior sagittal sinus and transverse sinus, the blood flow direction is along the head-to-tail direction and the left-to-right direction, respectively. The slice interval is set to the slice thickness of the image data. For high-resolution images with a slice thickness of 0.5 mm to 1 mm, the slice interval is consistent with the slice thickness; for image data with a larger slice thickness, additional slices can be generated between slices using interpolation methods to improve the accuracy of centerline extraction. In each slice layer, the luminal region is represented as a closed set of pixels, and all pixels within this set correspond to contrast agent or blood signals inside the lumen.

[0024] The centroid coordinates of the pixel set in the lumen region of each slice layer are calculated as the center point of that layer. The centroid calculation is achieved by weighted averaging of the coordinates of all pixels within the set. The weights can be the pixel's grayscale value, ensuring that regions with higher grayscale values ​​contribute more to the centroid position. For the i-th slice, assuming the lumen region contains N_i pixels, the coordinates of the j-th pixel are... If the grayscale value is I_{ij}, then the centroid coordinates of this layer are... The centroid coordinates are calculated based on the coordinates and grayscale values ​​of all pixels. The centroid coordinates, combined with the layer's position z_i in the blood flow direction, constitute the coordinates of the center point in three-dimensional space. .

[0025] All slices were arranged in order of blood flow direction, and the center point coordinates of each slice were connected sequentially to form an initial centerline. The initial centerline is a series of discrete three-dimensional spatial points, reflecting the overall orientation of the venous sinus lumen. Due to errors in the slicing process and centroid calculation, the initial centerline exhibits local non-smoothness or offset, requiring curve fitting to obtain a smooth geometric centerline. A cubic spline curve fitting method was employed. This method constructs a piecewise cubic polynomial to ensure that the fitted curve maintains continuous first and second derivatives at each node, thus obtaining a smooth curve shape. During the fitting process, each point of the initial centerline was used as a control point. The curve's position at the control points was close to the original point, while maintaining overall smoothness. The fitted geometric centerline accurately characterizes the spatial orientation of the venous sinus stenosis segment, providing a benchmark for subsequent stent implantation path planning.

[0026] Multiple sampling points are set at fixed intervals along the geometric centerline, typically 1mm to 2mm, to ensure that subtle changes in the lumen diameter are captured. At each sampling point, the lumen diameter of the cross-section perpendicular to the geometric centerline needs to be calculated. The plane perpendicular to the centerline is determined by calculating the tangent vector of the centerline at that point, which is calculated based on the positions of the adjacent points before and after that point. After determining the cross-sectional plane, the intersection line of this plane and the lumen boundary is extracted; the intersection line forms a closed annular profile. There are several ways to calculate the lumen diameter; one can use the maximum inscribed circle diameter of the profile, or calculate the equivalent circular diameter of the profile. The equivalent circular diameter is calculated based on the area of ​​the cross-section, which is obtained by counting the number of pixels in the lumen region within the cross-section and multiplying it by the physical size of the pixels. For irregularly shaped cross-sections, the equivalent circular diameter provides a more stable diameter measurement. The lumen diameters of each sampling point are arranged in order of their position on the centerline, forming a lumen diameter distribution sequence.

[0027] The derivative of the lumen diameter distribution sequence is calculated to obtain the lumen diameter change rate sequence. The derivative calculation uses a numerical differentiation method. For the k-th sampling point, the lumen diameter change rate is approximately calculated as the ratio of the diameter difference to the distance difference between adjacent points before and after that point. A positive value of the lumen diameter change rate indicates lumen expansion at that location, while a negative value indicates contraction. A characteristic of venous sinus stenosis is a significant decrease in lumen diameter over a short distance; therefore, the region of most severe stenosis corresponds to the location with the largest absolute value of the lumen diameter change rate. The location with the largest absolute value is found by traversing the lumen diameter change rate sequence, and this location is marked as the region of most severe stenosis. In some cases, multiple localized diameter contractions exist within the stenotic segment, requiring clinical criteria to determine whether multiple stenosis points need to be identified.

[0028] After identifying the area of ​​most severe stenosis, the stent deployment location needs to be planned. The stent deployment location cannot be directly placed at the point of most severe stenosis because a certain support segment is needed after deployment to ensure stent stability and effectiveness. The stent deployment location is determined by offsetting a predetermined distance from the area of ​​most severe stenosis in the proximal direction, i.e., the direction of blood flow origin. The predetermined distance is determined based on the stent model and length, typically set to 5mm to 10mm, ensuring that the proximal end of the stent is located in an area with a relatively normal lumen diameter, providing sufficient anchoring force. The stent implantation path starts from the deployment location and extends along the geometric centerline to the normal vascular region distal to the stenosis. The normal vascular region distal to the stenosis is identified by the lumen diameter distribution sequence; specifically, it is the area after the stenosis where the lumen diameter returns to near normal and remains stable.

[0029] In one optional implementation, the preoperative image data is processed using a gray-level gradient-based segmentation algorithm to extract the luminal boundary of the venous sinus. The luminal boundary is then sliced ​​layer by layer along the blood flow direction. In each slice layer, a set of pixels representing the luminal region is extracted, including: The preoperative image data is standardized for grayscale values, and the grayscale difference values ​​of each voxel point in three orthogonal directions are calculated. The grayscale difference values ​​in the three orthogonal directions are combined to form the grayscale gradient vector of the voxel point. The magnitude of the gray-level gradient vector is calculated as the gradient amplitude. Voxel points with gradient amplitudes greater than a preset gradient threshold are marked as boundary candidate points. The positions of the boundary candidate points where the gradient direction change angle is greater than the angle threshold are determined as the inflection points of the blood vessel wall boundary. A closed lumen boundary is constructed based on the inflection points of the blood vessel wall boundary. Obtain blood flow direction information of the venous sinus, establish a slice coordinate system along the blood flow direction, wherein the vertical axis of the slice coordinate system is parallel to the blood flow direction, and the horizontal plane is perpendicular to the blood flow direction; In the slice coordinate system, the cavity boundary is sliced ​​and sampled according to a fixed layer spacing. All voxel points intersecting the cavity boundary are extracted in the transverse plane of each slice layer. The coordinates of the voxel points are projected onto the two-dimensional plane of the slice layer. Boundary tracing is performed on the projected voxel coordinates to identify the voxel sequence that constitutes a closed contour, and all voxel points inside the closed contour are determined as the pixel set of the lumen region of the slice layer.

[0030] Because different imaging devices and scanning parameters can lead to variations in the grayscale range of preoperative image data, the preoperative image data is first standardized in terms of grayscale values. Specifically, the grayscale value distribution of all voxels in the image data is statistically analyzed, and the minimum and maximum grayscale values ​​are obtained. The original grayscale value of each voxel is then subtracted from the minimum value and divided by the grayscale value range, mapping the grayscale value to a standardized interval of 0 to 1. This standardization process eliminates grayscale differences under different scanning conditions, providing a unified numerical basis for subsequent gradient calculations.

[0031] For each standardized voxel, its gray-level difference value in three orthogonal directions is calculated. In a Cartesian coordinate system, these three orthogonal directions are the X-axis, Y-axis, and Z-axis. For a voxel at spatial positions x, y, z, its gray-level difference value in the X-axis direction is obtained by comparing the gray-level values ​​of this voxel with those of adjacent voxels in the X-axis direction; that is, calculating the difference in gray-level values ​​between voxels at positions x+1, y, z and those at positions x, y, z. Similarly, the corresponding gray-level difference values ​​are calculated in the Y-axis and Z-axis directions. The gray-level difference values ​​in the three orthogonal directions are combined as a vector to form the gray-level gradient vector of the voxel. The direction of this gradient vector indicates the direction of the most drastic gray-level change at that location.

[0032] To quantify the severity of grayscale changes, the magnitude of the grayscale gradient vector is calculated as the gradient amplitude. The gradient amplitude reflects the intensity of grayscale changes around the voxel. At the boundaries of anatomical structures such as the vessel wall, significant jumps in grayscale values ​​occur due to abrupt changes in tissue density, resulting in typically larger gradient amplitudes. A preset gradient threshold is set, determined based on typical contrast characteristics of venous sinus images, and is usually taken as a value between 15% and 25% of the normalized gradient amplitude range. Voxel points with gradient amplitudes greater than the preset gradient threshold are marked as boundary candidate points, forming an initial set of potential vessel wall boundary locations.

[0033] Further filtering is needed because the candidate boundary points contain noise or points that are not true boundaries. At the true vessel wall boundary, the gradient direction usually remains relatively stable or undergoes regular inflections. The angle of gradient direction change between each candidate boundary point and other candidate boundary points in its neighborhood is calculated; this angle is obtained by the angle between the two gradient vectors. An angle threshold of 30 to 45 degrees is set, and locations where the gradient direction change angle is greater than this threshold are identified as vessel wall boundary inflection points. These inflection points mark the key turning points of the vessel wall boundary, which are particularly important in tortuous or bifurcated venous sinus structures.

[0034] Based on the identified inflection points of the vessel wall boundary, a closed lumen boundary is constructed using region growing and boundary connection algorithms. Starting from each inflection point, adjacent candidate boundary points are searched along the direction perpendicular to the gradient. Adjacent inflection points are then connected sequentially using a shortest path algorithm to form a continuous boundary curve. For boundary segments with breaks, spline interpolation is used to fill in the missing parts, ensuring that the final lumen boundary is a complete and closed three-dimensional surface.

[0035] To perform slicing analysis of the luminal boundary along the blood flow direction, it is necessary to obtain the blood flow direction information of the venous sinuses. This blood flow direction information can be extracted from phase-contrast magnetic resonance angiography data or calculated by analyzing the centerline orientation of the lumen. Specifically, the skeletal centerline of the venous sinus lumen is extracted, which represents the main orientation path of the lumen. At each point on the centerline, the direction vector of the line connecting that point to its adjacent points is calculated; this direction vector represents the blood flow direction at that location.

[0036] Based on blood flow direction information, a slice coordinate system is established along the blood flow direction. The vertical axis of this slice coordinate system is set to be parallel to the blood flow direction to ensure that the sampling direction of the slice is consistent with the actual flow direction of the blood. The horizontal plane of the slice coordinate system is perpendicular to the blood flow direction, and this horizontal plane constitutes the cross-section of each slice layer. By setting this coordinate system, luminal cross-sectional information directly corresponding to hemodynamic characteristics can be obtained.

[0037] In the slice coordinate system, the lumen boundary is sliced ​​and sampled at a fixed slice interval, typically set to 0.5 mm to 1.0 mm. This value ensures sufficient spatial resolution to capture detailed changes in the lumen morphology while avoiding data redundancy caused by oversampling. Starting from the beginning of the venous sinus, a slice is established every fixed slice interval along the blood flow direction. In the transverse plane of each slice, all voxel points intersecting the lumen boundary are identified. These intersection points are determined by whether the voxel points simultaneously satisfy the conditions of being located on the lumen boundary surface and having their coordinates falling within the plane of the current slice.

[0038] The three-dimensional coordinates of the identified voxel points are projected onto the two-dimensional plane of the slice layer. During projection, the two coordinate components of the voxel points in the transverse plane are retained, while the longitudinal coordinate component along the blood flow direction is discarded, thus transforming the lumen boundary in three-dimensional space into a contour curve in the two-dimensional plane. Boundary tracing processing is then performed on the projected voxel point coordinates. Using either a chain code tracing algorithm or a contour tracing algorithm, starting from any projection point, adjacent projection points are searched sequentially in a counter-clockwise or clockwise direction to identify the voxel point sequence that forms a closed contour. The points in this sequence are arranged sequentially according to spatial continuity, forming a complete description of the lumen boundary on the slice layer.

[0039] Based on the identified closed contour, a region filling algorithm is used to determine the pixel set of the lumen region of the slice layer by identifying all voxel points within the closed contour. Specifically, starting from any point inside the contour, a seed filling algorithm is used to expand outwards, incorporating all points that have not crossed the contour boundary into the lumen region. The resulting pixel set completely represents the cross-sectional area of ​​the venous sinus lumen on the slice layer. The number of pixels in this set reflects the cross-sectional area of ​​the lumen at that location, and the spatial distribution characteristics of the pixels reflect the shape information of the lumen, providing a precise quantitative basis for subsequent assessment of stenosis and surgical planning.

[0040] In one optional implementation, the intraoperative real-time image data is registered with the pixel set of the lumen region. The registration transformation matrix is ​​calculated by extracting the spatial positional deviation between the vessel wall boundary feature points in the intraoperative real-time image data and the corresponding feature points in the pixel set of the lumen region, including: Determine the gray-level gradient vector of each pixel in the intraoperative real-time image data, and identify pixels whose gray-level gradient amplitude exceeds the boundary threshold as candidate points of the intraoperative vessel wall boundary. Connectivity analysis was performed on the candidate points of the intraoperative vessel wall boundary, and the point with the largest rate of curvature change on the boundary of the connected region was extracted as the feature point of the intraoperative vessel wall boundary. The spatial coordinates and local tangent direction of each feature point of the intraoperative vessel wall boundary were recorded. In the pixel set of the lumen region, identify the inflection point positions on the lumen contour of each slice layer, calculate the curvature value at the inflection point position, and determine the inflection point with a curvature value greater than the curvature threshold as the lumen boundary feature point; Establish an initial correspondence between the intraoperative vessel wall boundary feature points and the lumen boundary feature points. For each intraoperative vessel wall boundary feature point, search for the feature point in the lumen boundary feature point set that has the closest geometric position and the smallest local tangent direction angle as the corresponding point. Calculate the spatial position deviation between each pair of corresponding points, and decompose the spatial position deviation into a translation component along the coordinate axis and a rotation component around the coordinate axis. The global translation vector is obtained by weighted averaging the translation components of all corresponding point pairs, and the global rotation parameters are obtained by weighted averaging the rotation components of all corresponding point pairs. The weighting coefficients are determined based on the matching confidence between corresponding point pairs. A translation transformation matrix is ​​constructed based on the global translation vector, and a rotation transformation matrix is ​​constructed based on the global rotation parameters. The translation transformation matrix and the rotation transformation matrix are then multiplied to obtain the registration transformation matrix.

[0041] like Figure 2 As shown, the method includes: After completing the pixel set annotation of the lumen region, it is necessary to accurately register the intraoperative real-time image data with this pixel set to achieve spatial correspondence between preoperative planning and intraoperative navigation. The core of the registration process lies in extracting feature points with anatomical correspondences from the two data sources and calculating the registration transformation matrix based on the spatial positional deviations between these feature points.

[0042] For processing intraoperative real-time image data, grayscale gradient analysis is performed on each pixel. The Sobel or Prewitt operator is used to convolve the image data, calculating the grayscale change rate in the horizontal and vertical directions, thus obtaining the grayscale gradient vector for each pixel. This gradient vector contains two attributes: direction and amplitude. The amplitude reflects the degree of grayscale change at that location. In angiography images, there is a significant grayscale difference between the vessel wall and the contrast agent-filled area; therefore, the grayscale gradient amplitude corresponding to the vessel wall boundary is usually large. A boundary threshold of 200 (within the grayscale range of 0-4095) is set, and pixels with grayscale gradient amplitudes exceeding this threshold are marked as candidate points for the intraoperative vessel wall boundary. The specific value of the boundary threshold can be adjusted according to the imaging characteristics of the image acquisition equipment and the contrast agent concentration. Typically, a value is chosen that allows the candidate points to completely cover the vessel wall boundary area without containing too many noise points.

[0043] After obtaining candidate points for the intraoperative vessel wall boundary, connected component analysis is performed on these candidate points. A region growing algorithm or a connected component labeling algorithm is used to assign spatially adjacent candidate points to the same connected component. For each connected component, a contour curve is extracted along its boundary, and the local curvature of each point on the curve is calculated. Curvature calculation can be achieved by fitting a local circular arc or using a difference method. Specifically, for a point on the boundary, the neighboring points within a range of 5 pixels before and after it are taken, and a quadratic curve is fitted using the least squares method. The second derivative of this curve reflects the curvature value of that point. The rate of change of curvature between adjacent points is further calculated, i.e., the first difference of curvature. Local maxima of the rate of change of curvature appear at vessel bifurcation, the entrance to a stenotic segment, or at sharp bends in the vessel. The point with the largest rate of change of curvature on the boundary of the connected component is identified as the intraoperative vessel wall boundary feature point. The three-dimensional spatial coordinates of each feature point are recorded, and the local tangent direction is obtained by calculating the direction of the line connecting the neighboring points before and after that point. This direction is represented by a unit vector.

[0044] For the pixel set of the lumen region, it is necessary to extract the lumen boundary feature points corresponding to the intraoperative vessel wall boundary feature points. Since the lumen region is stored in the form of slices, the lumen contour of each slice is processed layer by layer. Polygon fitting is performed on the lumen contour of a single slice to identify the inflection point positions on the contour. Inflection point identification can be achieved by calculating the angle change between adjacent line segments on the contour; when the angle change exceeds 15 degrees, it is determined to be an inflection point. For each inflection point position, the curvature value of the point is calculated using an arc fitting method. Specifically, the inflection point and three contour points before and after it are taken, for a total of seven points, and the optimal arc is fitted. The reciprocal of the arc radius is the curvature value. The curvature threshold is set to 0.08 pixels to the power of negative one, and inflection points with curvature values ​​greater than this threshold are identified as lumen boundary feature points. The curvature threshold is selected based on preserving the main geometric feature points of the vessel morphology. Too small a threshold will lead to too much noise introduced by the feature points, while too large a threshold will miss important anatomical landmarks.

[0045] When establishing the correspondence between intraoperative vessel wall boundary feature points and lumen boundary feature points, a strategy combining nearest neighbor search and directional constraints is employed. For each intraoperative vessel wall boundary feature point, the Euclidean distance is calculated within the set of lumen boundary feature points, initially selecting the top 10 closest candidate corresponding points. Further calculations are made of the angle between the local tangent direction of the intraoperative feature point and the local tangent directions of each candidate corresponding point, selecting the candidate point with the smallest angle as the final corresponding point. This method is based on the assumption that the corresponding anatomical locations are not only spatially close but also have consistent local geometry. When the angle is less than 30 degrees and the Euclidean distance is less than 5 mm, it is considered a valid corresponding point pair; otherwise, it is considered that the intraoperative feature point has no reliable corresponding point in the set of lumen boundary feature points and is excluded.

[0046] For each pair of valid corresponding points, calculate its spatial position deviation vector, which is a three-dimensional vector pointing from the feature point at the lumen boundary to the feature point at the intraoperative vessel wall boundary. This deviation vector is decomposed into translational components along the X, Y, and Z axes, and rotational components around these three coordinate axes. The rotational components are calculated by comparing the local coordinate system orientation differences of the corresponding point pairs, specifically using a rotation vector representation, where the direction of the rotation vector is the rotation axis and the magnitude is the rotation angle.

[0047] The translation and rotation components of all valid corresponding point pairs are weighted and averaged. The weighting coefficients are determined based on the matching confidence of the corresponding point pairs. The matching confidence considers three factors: the smaller the Euclidean distance between the corresponding point pairs, the higher the confidence; the smaller the angle between the local tangent directions, the higher the confidence; and the larger the area of ​​the connected region where the intraoperative feature point is located, the higher the confidence. Specifically, the distance is normalized to the range of 0 to 1, and its reciprocal is used as the distance term weight. The cosine of the angle is used as the direction term weight, and the normalized area of ​​the connected region is used as the area term weight. The product of these three weights is the comprehensive matching confidence. The X-axis translation components of all corresponding point pairs are weighted and averaged to obtain the global X-direction translation. The Y-axis and Z-axis translation components are processed similarly. The translations in the three directions constitute the global translation vector. When weighting and averaging the rotation components of all corresponding point pairs, the rotation vector is first converted to quaternion form. The quaternions are then weighted, summed, normalized, and converted back to rotation vector form to obtain the global rotation parameters.

[0048] A translation transformation matrix is ​​constructed based on the global translation vector. This matrix is ​​a 4×4 homogeneous coordinate transformation matrix, with the first three rows and first three columns being the identity matrix, and the first three rows of the fourth column containing the translation amounts in the X, Y, and Z directions, respectively. A rotation transformation matrix is ​​constructed based on the global rotation parameters. According to the rotation axis direction and rotation angle of the rotation vector, the Rodrigues formula is used to convert the rotation vector into a 3×3 rotation matrix, which is then extended into a 4×4 homogeneous coordinate transformation matrix.

[0049] In one optional implementation, the registration transformation matrix is ​​applied to map the stent implantation path and the stent release position to the coordinate system of the intraoperative real-time image data, including: Extract the spatial coordinates of each sampling point on the stent implantation path, organize the spatial coordinates of each sampling point into a coordinate matrix, apply the registration transformation matrix to the coordinate matrix to perform matrix operations, and obtain the spatial position of the mapped stent implantation path in the intraoperative real-time image data coordinate system. The coordinate transformation matrix is ​​applied to the coordinates of the stent release position to obtain the corresponding coordinates of the stent release position in the intraoperative real-time image data coordinate system, and the corresponding coordinates are marked as the intraoperative target release point.

[0050] After designing the stent implantation path and determining the stent release location during the preoperative planning stage, this preoperative planning data needs to be mapped to the coordinate system of the intraoperative real-time imaging data to provide the surgeon with accurate navigation information during the operation. This mapping process relies on the application of a registration transformation matrix, which uses matrix operations to achieve spatial position transformation between different coordinate systems.

[0051] For mapping the stent implantation path, the path undergoes discretization sampling. The stent implantation path is typically a three-dimensional curve in space, which can be a straight line, a smooth curve, or a polygonal line composed of multiple line segments. To accurately describe the path's geometry, sampling is performed along the path direction at certain intervals. The sampling interval can be determined based on the path's complexity and accuracy requirements, typically set to 0.5 mm to 2 mm. For areas with greater curvature, the sampling density can be appropriately increased to ensure the accuracy of the path's shape. Through sampling, the continuous path is transformed into a series of discrete spatial points, each carrying its three-dimensional spatial coordinates in the preoperative image coordinate system.

[0052] After extracting the spatial coordinates of each sampling point, these coordinate data are organized into a matrix for batch processing. Assume N points are sampled along the stent implantation path, and the coordinates of the i-th sampling point are x_i, y_i, z_i, where i ranges from 1 to N. The coordinates of all sampling points are constructed into an N x 4 column homogeneous coordinate matrix P. Each row of the matrix represents the homogeneous coordinates x_i, y_i, z_i, and 1 of a sampling point. The added value 1 in the last column represents the standard form of homogeneous coordinate representation, used to support affine transformations involving translation components.

[0053] When performing matrix operations on a coordinate matrix using a registration transformation matrix, the registration transformation matrix M is multiplied by the coordinate matrix P. The registration transformation matrix M is a 4x4 square matrix containing transformation parameters such as rotation, scaling, and translation. Matrix operations are then performed. In the form of, This represents the transpose of the registration transformation matrix, and the result is... This is the mapped coordinate matrix. Alternatively, it can be adopted depending on the actual coordinate organization method. The specific form of the operation depends on whether the coordinate matrix organizes the point coordinates by rows or columns. Through this matrix operation, the conversion of all sampled points from the preoperative image coordinate system to the intraoperative real-time image coordinate system is completed in batches.

[0054] The result of matrix operations Even using homogeneous coordinates, each transformed point needs to be restored to ordinary 3D coordinates. For the i-th row of the resulting matrix (x'_i, y'_i, z'_i, w_i), homogeneous coordinate normalization is performed by dividing the first three components by the fourth component (w_i) to obtain the true 3D coordinates of the sampled point in the intraoperative real-time image coordinate system: x'_i / w_i, y'_i / w_i, z'_i / w_i. In most cases, the fourth row of the registration transformation matrix is ​​0, 0, 0, 1, in which case w_i is always 1, and the coordinate values ​​do not require additional normalization.

[0055] By connecting all the transformed sampling point coordinates in the original path order, the spatial position of the stent implantation path in the intraoperative real-time image coordinate system can be reconstructed. This mapped path is in the same coordinate system as the intraoperative vascular image and can be directly superimposed on the intraoperative real-time image, providing the physician with guidance information for catheter insertion. The path can be visualized using curves or striped areas of different colors to intuitively show the vascular path the stent should pass through.

[0056] The mapping of stent deployment locations is handled similarly to that of path sampling points but more simply. The stent deployment location is determined as a specific point within the blood vessel during preoperative planning, and its coordinates are represented as x_r, y_r, z_r in the preoperative image coordinate system. These coordinates are then converted to homogeneous coordinates x_r, y_r, z_r, 1, and the registration transformation matrix M is directly applied for coordinate transformation. Specifically, the homogeneous coordinate vector is multiplied by the registration transformation matrix to obtain the transformed homogeneous coordinate vector, which is then normalized to obtain the corresponding coordinates x'_r, y'_r, z'_r of the stent deployment location in the intraoperative real-time image coordinate system.

[0057] The obtained coordinates are marked as the intraoperative target release point. This point is displayed on the real-time intraoperative images as a special graphic symbol, such as a cross, a circle, or a flashing highlight. The target release point should be visually salient enough for the surgeon to quickly identify it during the procedure. Simultaneously, numerical information can be displayed around the mark, including the distance from the point to the current stent tip position, the estimated catheter length required to reach the point, and other auxiliary data to help the surgeon precisely control the timing of stent release.

[0058] In practical applications, multiple reference points within a certain distance from the target release point along the stent implantation path can be calculated as preparation areas for stent release. For example, warning markers can be placed within a path segment 5 to 10 millimeters in front of the target release point. When the stent tip enters this area, the system issues an audible or visual prompt, reminding the physician to slow down the pushing speed and prepare for stent release. This tiered prompting mechanism effectively improves the accuracy and safety of stent release operations.

[0059] Through the mapping process described above, the stent implantation path and release location planned in the preoperative static images are accurately transformed into the coordinate system of the intraoperative dynamic images, achieving seamless integration between preoperative planning and intraoperative operation. The mapped navigation information is presented on the intraoperative images in real time overlay. Based on the displayed path guidance and target release point prompts, the surgeon can precisely control the advancement of the catheter and stent, significantly improving the operational precision and success rate of interventional surgery.

[0060] In one optional implementation, during the catheter stent delivery process, the spatial coordinates of the catheter tip are acquired in real time, the deviation between the spatial coordinates and the stent implantation path is calculated, and when the deviation exceeds a safety threshold, a path correction command is generated. The catheter pushing direction is adjusted according to the path correction command, including: The position information of the catheter tip is acquired in real time by intraoperative imaging equipment, and the position information is converted into spatial coordinates in the intraoperative real-time imaging data coordinate system; Calculate the shortest distance from the spatial coordinates to the stent implantation path, determine the projection point on the stent implantation path that is closest to the spatial coordinates, and define the shortest distance as the lateral deviation value; Calculate the difference between the projected length of the spatial coordinates along the tangent direction of the stent implantation path and the arc length position of the projection point on the stent implantation path, and define the difference as the longitudinal deviation value; The horizontal deviation value and the vertical deviation value are vectorized to obtain a comprehensive deviation value, and it is determined whether the comprehensive deviation value exceeds a preset safety threshold. When the overall deviation value exceeds the safety threshold, a deviation correction vector is calculated from the spatial coordinates to the projection point, and the spatial direction that the catheter tip needs to be adjusted is determined based on the deviation correction vector. The angle that needs to be changed for the catheter pushing operation is calculated based on the spatial direction. The angle includes the horizontal deflection angle and the vertical deflection angle relative to the current pushing direction.

[0061] Catheter stent delivery is a core procedure in interventional surgery, and real-time monitoring of the catheter tip's position and control of path deviation directly affect surgical precision and patient safety. During the procedure, imaging data of the catheter tip is continuously acquired using C-arm X-ray machines, angiography equipment, or intraoperative CT scans. These devices output two-dimensional or three-dimensional images at a frequency of 15 to 30 frames per second. The image processing unit performs feature recognition on the acquired image frames, extracting the two-dimensional pixel coordinates by identifying high-contrast markers or metallic contrast rings at the catheter tip. When using biplane angiography, two-dimensional coordinates from both anteroposterior and lateral angles are acquired simultaneously, and the three-dimensional spatial position of the catheter tip is reconstructed using triangulation principles. Converting the pixel coordinates to spatial coordinates within the intraoperative real-time image data coordinate system requires applying the imaging equipment's calibration parameters, including intrinsic parameters such as focal length, principal point position, and distortion coefficients, as well as extrinsic parameters such as the rotation matrix and translation vector of the imaging equipment relative to the operating table. The resulting spatial coordinates are in millimeters, with the origin typically set at a specific anatomical landmark on the patient's body, such as the femoral artery puncture site or the center of the heart.

[0062] Deviation calculation requires establishing the spatial geometric relationship between the real-time position of the catheter tip and the pre-defined stent implantation path. The stent implantation path is defined as a curve in three-dimensional space during the preoperative planning stage. This curve is stored parametrically and can be a smooth curve generated by spline interpolation from a series of control points. When calculating the shortest distance from the spatial coordinates of the catheter tip to this path curve, an iterative optimization algorithm searches for the point closest to that coordinate on the path curve; this point is the projection point. Specifically, the path curve is discretized into dense sampling points. The Euclidean distance from the spatial coordinates to each sampling point is calculated, and the sampling point with the smallest distance is found as the initial projection point. Then, a local refinement search is performed near this point, iteratively optimizing using gradient descent or Newton's method until convergence to the true shortest distance point. The Euclidean distance between the spatial coordinates and the projection point is the lateral deviation value, reflecting the degree of deviation of the catheter tip in the direction perpendicular to the path.

[0063] The calculation of longitudinal deviation involves the positional error of the catheter tip in the path advancement direction. The tangential direction of the stent implantation path is calculated at the projection point, representing the path's forward direction at that point. The spatial coordinates of the catheter tip relative to the path's starting point are projected onto this tangential direction to obtain the projected length along the path direction. Simultaneously, the projection point has a defined arc length position on the stent implantation path, which is the cumulative length from the path's starting point along the curve to the projection point. The difference between the projected length and the arc length position is the longitudinal deviation value; a positive value indicates the catheter tip is ahead of the ideal position, while a negative value indicates it is behind. This parameter is crucial for controlling the timing of stent release, as the stent must be released only when it reaches the precise location of the lesion.

[0064] The composite deviation value integrates lateral and longitudinal deviations into a single deviation metric using a vector synthesis method. The lateral deviation value is treated as one component of a vector, and the longitudinal deviation value as the other. The square root of the sum of the squares of these two components is calculated to obtain the scalar magnitude of the composite deviation value. In some implementations, different weighting coefficients can be assigned to lateral and longitudinal deviations; for example, the weight of lateral deviation can be set to 1.2, and the weight of longitudinal deviation to 0.8, to reflect the clinical characteristic that lateral deviation has a greater impact on surgical risk. The composite deviation value is compared with a preset safety threshold, which is determined based on factors such as vessel diameter, lesion type, and stent size, and is typically set within the range of 2 to 5 mm. For smaller coronary arteries, the safety threshold is set to 2 mm, while for large vessels such as the aorta, the safety threshold can be relaxed to 5 mm.

[0065] When the overall deviation exceeds the safety threshold, the system determines that there is a deviation in the catheter delivery path that needs to be corrected, and initiates the path correction procedure. The vector pointing from the spatial coordinates of the catheter tip to the projection point is the deviation correction vector. The direction of this vector indicates the target direction in which the catheter tip needs to move, and the magnitude of the vector represents the distance that needs to be corrected. The deviation correction vector is decomposed on the three orthogonal axes of the intraoperative coordinate system to obtain components in the left-right, front-back, and up-down directions. Based on these components, the spatial direction in which the catheter tip needs to be adjusted is determined. For example, when the left-right component is positive and the front-back component is negative, it indicates that the catheter needs to be adjusted to the right front.

[0066] The angle adjustment for catheter delivery is calculated based on the spatial angle difference between the current delivery direction of the catheter tip and the target correction direction. The current delivery direction can be obtained by fitting the movement trajectory of the catheter tip in the most recent few frames, or directly using the tangent direction of the stent implantation path at the current position. The angle between the current delivery direction and the deviation correction vector is calculated, and this angle is decomposed into a horizontal deflection angle and a vertical deflection angle. The horizontal deflection angle is calculated by projecting the two direction vectors onto the horizontal plane and calculating their angle, ranging from -180 degrees to +180 degrees. A positive value indicates a rightward deflection, and a negative value indicates a leftward deflection. The vertical deflection angle is calculated by calculating the angle between the direction vector and the horizontal plane. A positive value indicates an upward lift, and a negative value indicates a downward press. These angle values ​​are converted into operational commands and transmitted to the control system of the robotic arm or displayed on the surgical navigation interface for the surgeon's reference, enabling precise adjustment of the catheter delivery direction, ensuring that the catheter tip returns to the safe path range, and ultimately delivering the stent accurately to the target position.

[0067] A second aspect of the present invention provides a navigation and positioning system for the preparation and implantation of a cerebral venous sinus stent, comprising: The image acquisition unit is used to acquire preoperative image data and intraoperative real-time image data of the patient's cerebral venous sinus. Based on the preoperative image data, it determines the pixel set of the lumen region and identifies the geometric center line of the stenotic segment of the venous sinus in the pixel set of the lumen region. It calculates the stent implantation path according to the spatial orientation of the geometric center line and determines the stent release position based on the lumen diameter change rate of the stenotic segment. The stent release position corresponds to a preset distance in front of the proximal end of the vascular segment with the largest lumen diameter change rate. The registration transformation unit is used to register the intraoperative real-time image data with the pixel set of the lumen region. It calculates the registration transformation matrix by extracting the spatial position deviation between the feature points of the vessel wall boundary in the intraoperative real-time image data and the corresponding feature points in the pixel set of the lumen region. The stent delivery unit is used to apply the registration transformation matrix to map the stent implantation path and the stent release position to the coordinate system of the intraoperative real-time image data. During the stent delivery process, the spatial coordinates of the catheter tip are acquired in real time, the deviation between the spatial coordinates and the stent implantation path is calculated, and when the deviation exceeds a safety threshold, a path correction command is generated. The catheter pushing direction is adjusted according to the path correction command until the catheter tip reaches the stent release position, and the stent is released at the stent release position.

[0068] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0069] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0070] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions 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 invention.

Claims

1. A navigation and positioning system for the fabrication and implantation of a cerebral venous sinus stent, characterized in that, include: The image acquisition unit is used to acquire preoperative image data and intraoperative real-time image data of the patient's cerebral venous sinus. Based on the preoperative image data, it determines the pixel set of the lumen region and identifies the geometric center line of the stenotic segment of the venous sinus in the pixel set of the lumen region. It calculates the stent implantation path according to the spatial orientation of the geometric center line and determines the stent release position based on the lumen diameter change rate of the stenotic segment. The stent release position corresponds to a preset distance in front of the proximal end of the vascular segment with the largest lumen diameter change rate. The registration transformation unit is used to register the intraoperative real-time image data with the pixel set of the lumen region. It calculates the registration transformation matrix by extracting the spatial position deviation between the feature points of the vessel wall boundary in the intraoperative real-time image data and the corresponding feature points in the pixel set of the lumen region. The stent delivery unit is used to apply the registration transformation matrix to map the stent implantation path and the stent release position to the coordinate system of the intraoperative real-time image data. During the stent delivery process, the spatial coordinates of the catheter tip are acquired in real time, the deviation between the spatial coordinates and the stent implantation path is calculated, and when the deviation exceeds a safety threshold, a path correction command is generated. The catheter pushing direction is adjusted according to the path correction command until the catheter tip reaches the stent release position, and the stent is released at the stent release position.

2. The system according to claim 1, characterized in that, The image acquisition unit is further configured to: The preoperative image data is processed using a gray-level gradient-based segmentation algorithm to extract the luminal boundary of the venous sinus. The luminal boundary is sliced ​​layer by layer along the blood flow direction. The pixel set of the luminal region is extracted in each slice layer, and the centroid coordinates of the pixel set are calculated as the center point coordinates of that layer. The center point coordinates of each slice layer are connected sequentially according to the blood flow direction to form an initial center line. The geometric center line of the venous sinus stenosis segment is obtained by fitting the initial center line with a cubic spline curve. Multiple sampling points are set at fixed intervals along the geometric center line, and the cross-sectional diameter of the lumen perpendicular to the geometric center line is calculated at each sampling point to obtain the lumen diameter distribution sequence; The derivative of the lumen diameter distribution sequence is calculated to obtain the lumen diameter change rate sequence, and the position with the largest absolute value of the change rate in the lumen diameter change rate sequence is identified as the most severe stenosis region. Using the most severely stenotic area as a reference, the stent release position is determined by offsetting a preset distance in the proximal direction. Starting from the stent release position, the stent is extended along the geometric center line to the normal blood vessel area at the distal end of the stenotic segment, and the extension path is determined as the stent implantation path.

3. The system according to claim 2, characterized in that, The image acquisition unit is further configured to: The preoperative image data is standardized for grayscale values, and the grayscale difference value of each voxel point in three orthogonal directions is calculated. The grayscale difference values ​​in the three orthogonal directions are combined to form the grayscale gradient vector of the voxel point. The magnitude of the gray-level gradient vector is calculated as the gradient magnitude. Voxel points with gradient magnitudes greater than a preset gradient threshold are marked as boundary candidate points. The positions of the boundary candidate points where the gradient direction change angle is greater than the angle threshold are determined as the inflection points of the blood vessel wall boundary. A closed lumen boundary is constructed based on the inflection points of the blood vessel wall boundary. Obtain blood flow direction information of the venous sinus, establish a slice coordinate system along the blood flow direction, wherein the vertical axis of the slice coordinate system is parallel to the blood flow direction, and the horizontal plane is perpendicular to the blood flow direction; In the slice coordinate system, the cavity boundary is sliced ​​and sampled according to a fixed layer spacing. All voxel points intersecting the cavity boundary are extracted in the transverse plane of each slice layer. The coordinates of the voxel points are projected onto the two-dimensional plane of the slice layer. Boundary tracing is performed on the projected voxel coordinates to identify the voxel sequence that constitutes the closed contour, and all voxel points inside the closed contour are determined as the pixel set of the lumen region of the slice layer.

4. The system according to claim 1, characterized in that, The registration transformation unit is further used for: Determine the gray-level gradient vector of each pixel in the intraoperative real-time image data, and identify pixels whose gray-level gradient amplitude exceeds the boundary threshold as candidate points of the intraoperative vessel wall boundary; perform connected component analysis on the candidate points of the intraoperative vessel wall boundary, extract the point with the largest rate of curvature change on the boundary of the connected component as the feature point of the intraoperative vessel wall boundary, and record the spatial coordinates and local tangent direction of each feature point of the intraoperative vessel wall boundary. In the pixel set of the lumen region, identify the inflection point positions on the lumen contour of each slice layer, calculate the curvature value at the inflection point position, and determine the inflection point with a curvature value greater than the curvature threshold as the lumen boundary feature point; Establish an initial correspondence between the intraoperative vessel wall boundary feature points and the lumen boundary feature points. For each intraoperative vessel wall boundary feature point, search for the feature point in the lumen boundary feature point set that has the closest geometric position and the smallest local tangent direction angle as the corresponding point. Calculate the spatial position deviation between each pair of corresponding points, and decompose the spatial position deviation into a translation component along the coordinate axis and a rotation component around the coordinate axis. The global translation vector is obtained by weighted averaging of the translation components of all corresponding point pairs, and the global rotation parameters are obtained by weighted averaging of the rotation components of all corresponding point pairs. The weighting coefficients are determined based on the matching confidence between corresponding point pairs. A translation transformation matrix is ​​constructed based on the global translation vector, and a rotation transformation matrix is ​​constructed based on the global rotation parameters. The translation transformation matrix and the rotation transformation matrix are then multiplied to obtain the registration transformation matrix.

5. The system according to claim 1, characterized in that, The stent delivery unit is also used for: Extract the spatial coordinates of each sampling point on the stent implantation path, organize the spatial coordinates of each sampling point into a coordinate matrix, apply the registration transformation matrix to the coordinate matrix to perform matrix operations, and obtain the spatial position of the mapped stent implantation path in the intraoperative real-time image data coordinate system. The coordinate transformation matrix is ​​applied to the coordinates of the stent release position to obtain the corresponding coordinates of the stent release position in the intraoperative real-time image data coordinate system, and the corresponding coordinates are marked as the intraoperative target release point.

6. The system according to claim 5, characterized in that, The stent delivery unit is also used for: The position information of the catheter tip is acquired in real time by intraoperative imaging equipment, and the position information is converted into spatial coordinates in the intraoperative real-time imaging data coordinate system; Calculate the shortest distance from the spatial coordinates to the stent implantation path, determine the projection point on the stent implantation path that is closest to the spatial coordinates, and define the shortest distance as the lateral deviation value; Calculate the difference between the projected length of the spatial coordinates along the tangent direction of the stent implantation path and the arc length position of the projection point on the stent implantation path, and define the difference as the longitudinal deviation value; The horizontal deviation value and the vertical deviation value are vectorized to obtain a comprehensive deviation value, and it is determined whether the comprehensive deviation value exceeds a preset safety threshold. When the overall deviation value exceeds the safety threshold, a deviation correction vector is calculated from the spatial coordinates to the projection point, and the spatial direction that the catheter tip needs to be adjusted is determined based on the deviation correction vector. The angle that needs to be changed for the catheter pushing operation is calculated based on the spatial direction. The angle includes the horizontal deflection angle and the vertical deflection angle relative to the current pushing direction.

Images