A real-time magnetic resonance navigation system for neurosurgery

Abstract

The present invention relates to the technical field of surgical navigation, and particularly to a real-time magnetic resonance navigation system for neurosurgical operations. The system includes an image acquisition module for obtaining intraoperative continuous-frame MRI images; a dynamic region recognition module for determining the instrument connected domain through the boundary rule distribution of the connected domain and combining the endpoint movement conditions in continuous frames to determine the affected position; determining the traction region range through the local tissue edge distribution of the affected position, considering the dynamic change conditions of the previous multiple-frame ranges, and determining the intraoperative dynamic region according to the approximate distribution of the affected position; a quality adjustment module for performing gradient attenuation analysis on the distribution of the intraoperative dynamic region and the remaining regions under multiple frames of images before the current moment to determine the transmission quality of the connected region for transmission display navigation. The present invention locates the intraoperative affected region after tracking the instrument region, dynamically adjusts the quality of different regions, and balances the problems of transmission quality and real-time performance.

Description

Technical Field

[0001] The present invention relates to the technical field of surgical navigation, and particularly to a real-time magnetic resonance navigation system for neurosurgery. Background Art

[0002] Neurosurgery is a type of surgery involving the central nervous system (the brain and spinal cord) and its related structures, mainly used to treat diseases related to the brain, spinal cord, nerves, and their blood vessels. Neurosurgery not only requires extremely high skills from surgeons, but also the support of imaging techniques and surgical navigation systems is crucial to assist in improving the accuracy of surgery.

[0003] Navigation during the surgical process can be accurately carried out through imaging techniques. However, since real-time image scanning is required for navigation, the overall computational gap is relatively large. If hardware is upgraded, the cost will be significantly increased. Usually, in order to meet the immediacy requirement, the image quality needs to be reduced, but reducing the image quality will in turn affect surgical navigation, and the balance between image quality and real-time performance during transmission is relatively poor. Summary of the Invention

[0004] In order to solve the technical problem of the relatively poor balance between image quality and real-time performance during transmission in the prior art, the purpose of the present invention is to provide a real-time magnetic resonance navigation system for neurosurgery, and the specific technical solutions adopted are as follows:

[0005] The present invention provides a real-time magnetic resonance navigation system for neurosurgery, and the system includes:

[0006] An image acquisition module, configured to acquire intraoperative continuous-frame MRI images;

[0007] A dynamic region recognition module, configured to determine the instrument connected region of each frame of MRI image according to the boundary rule distribution of the connected regions in each frame of MRI image; determine the position of the surgical site of each frame of MRI image by analyzing the movement degree of the end points of the instrument connected region in the continuous-frame MRI images;

[0008] Determine the traction influence region of the surgical site according to the direction distribution of the edges within the local range at the surgical site of each frame of MRI image; obtain the intraoperative dynamic region of each frame of MRI image through the proximity degree between the continuous-frame MRI images before each frame of MRI image at the surgical sites;

[0009] A quality adjustment module, configured to determine the gradient attenuation quality of the connected regions in each frame of MRI image according to the position distribution of the intraoperative dynamic region of the surgical site and other connected regions in each frame of MRI image; determine the transmission quality of each connected region at the current moment according to the gradient attenuation quality of each connected region except the instrument connected region in the MRI image at the current moment in different MRI images;

[0010] A navigation execution module, which is used to update the MRI image based on the transmission quality of different connected regions at the current moment, and obtain an intraoperative transmission image to acquire a navigation path.

[0011] Further, the method for obtaining the instrument connected region includes:

[0012] For any connected region in each frame of MRI image, taking any pixel point on the boundary of the connected region as a starting point, and forming a sequence of pixel points continuously distributed clockwise along the boundary as the boundary sequence of the connected region;

[0013] In the boundary sequence, when the gradient direction between each pixel point and the next pixel point is different, the corresponding pixel point is taken as the turning pixel point of the connected region; count the number of turning pixel points and perform normalization processing to obtain the turning degree of the connected region.

[0014] Taking the connected region with the smallest turning degree in each frame of MRI image as the instrument connected region.

[0015] Further, the method for obtaining the position of the patient to be treated includes:

[0016] Performing linear fitting on the instrument connected regions in each frame of MRI image to obtain the instrument straight line of each frame of MRI image;

[0017] For any one frame of MRI image, successively taking one end point of the instrument straight line in the MRI image as an analysis point; calculating the displacement between the instrument straight lines of adjacent frames of MRI before the MRI image at the analysis points and calculating the mean value to obtain the moment of inertia value of the MRI image at the analysis point;

[0018] Taking the end point with the smallest moment of inertia value in the MRI image as the position of the patient to be treated in the MRI image.

[0019] Further, the method for obtaining the traction influence region includes:

[0020] For the position of the patient to be treated in any MRI image, within a preset local range at the position of the patient to be treated, marking the existing edges as the involved edges at the position of the patient to be treated; obtaining the extension direction of each involved edge at the position of the patient to be treated;

[0021] For any involved edge at the position of the patient to be treated, calculating the mean similarity between the involved edge and adjacent involved edges in the extension direction to obtain the involvement significance of the involved edge;

[0022] When the involvement significance is greater than a preset significance threshold, taking the corresponding involved edge as an influence edge; merging the connected regions where the image edges are located at the position of the patient to be treated to obtain the traction influence region at the position of the patient to be treated.

[0023] Further, the method for obtaining the intraoperative dynamic region includes:

[0024] For any frame of MRI image, each frame of MRI image before this MRI image is sequentially used as the analysis image;

[0025] Calculate the Euclidean distance between the position of the operation in this MRI image and the analysis image as the proximity parameter of the analysis image; the analysis image with the proximity parameter less than the preset proximity threshold is used as the approximate image of this MRI image;

[0026] Superimpose the traction influence region in the approximate image and the traction influence region of this MRI image to obtain the intraoperative dynamic region of this MRI image.

[0027] Further, the method for obtaining the gradient attenuation quality includes:

[0028] For any frame of MRI image, take the position of the operation in this MRI image as the center of the circle, and take the farthest distance from the center of the circle to the boundary of the intraoperative dynamic region as the radius to make a circle to determine the boundary of the highest quality range of this MRI image; the quality coefficient within the boundary of the highest quality range is the preset maximum quality coefficient;

[0029] Calculate the distance between the position of the operation in this MRI image and the boundary of each other connected domain, make a circle with the length of the farthest distance from the boundary of the other connected domain as the radius to determine the boundary of the maximum loss quality range of this MRI image; the quality coefficient on the boundary of the maximum loss quality range is the preset minimum quality coefficient;

[0030] Attenuate the quality coefficient between the boundary of the highest quality range and the boundary of the maximum loss quality range to obtain the quality coefficient at each position;

[0031] Calculate the mean value of the quality coefficients existing in each connected domain in this MRI image as the gradient attenuation quality of each connected domain.

[0032] Further, the attenuating the quality coefficient between the boundary of the highest quality range and the boundary of the maximum loss quality range to obtain the quality coefficient at each position includes:

[0033] Take the shortest connection line from the boundary of the highest quality range to the boundary of the maximum loss quality range as the gradient descent line;

[0034] Starting from the quality coefficient of the boundary of the highest quality region, use the attenuation function to attenuate the quality coefficient along the gradient descent line to the quality coefficient on the boundary of the maximum loss quality range to determine the quality coefficient at each position on the gradient descent line.

[0035] Further, the method for obtaining the transmission quality includes:

[0036] In the current - moment MRI image, the maximum gradient - attenuation quality of each connected region (except for the instrument - connected region) in all other MRI images is used as the transmission quality of each connected region at the current moment.

[0037] Furthermore, the method for obtaining the intraoperative transmission image includes:

[0038] Taking the MR image at the current moment and the transmission - quality situation as inputs, and outputting the intraoperative transmission image through a trained image - transmission model.

[0039] Furthermore, obtaining the extension direction of each traction edge at the position of the operation includes:

[0040] Performing a linear fitting on each traction edge, and taking the direction of the fitted straight line as the extension direction of each traction edge.

[0041] The present invention has the following beneficial effects:

[0042] The present invention determines the surgical - tool part through the boundary - regular - distribution situation of the connected regions and the motion situation of consecutive frames, and then tracks and locates the position of the operation. Subsequently, it is convenient to analyze the operation area at the position of the operation to ensure high - quality development of local images. Further, it ensures that the areas affected by tissue traction during the operation can also be observed and analyzed. Based on the local - deformation edges that will occur at the position of the operation, the range of the traction area of the operation is determined through the distribution of the local - tissue edges at the position of the operation, and considering the operation - range situations involved in the previous steps, the intraoperative dynamic area affected during the operation is determined according to the distribution proximity of the position of the operation, making the key - attention area in each frame of the image more accurate. Then, through the gradient - attenuation analysis of the distribution between the key - attention intraoperative dynamic area and the remaining areas in each frame of the image, the attenuation - quality situation is obtained, and considering the integrity and consistency of the regional tissue during the operation, the transmission quality of the final connected region is determined through the gradient - attenuation quality situations in different image frames before the current moment for transmission - display navigation. The present invention locates the intraoperative - affected area after tracking the instrument area, dynamically adjusts the quality of different areas, and balances the problems of transmission quality and real - time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following - described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 It is a structural block diagram of a real - time magnetic - resonance navigation system for a neurosurgical operation provided by an embodiment of the present invention;

[0045] Figure 2 A basic operation flowchart of real-time navigation in existing neurological surgeries provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a global high-quality MRI image provided by an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of some surgical tool instruments provided by an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of a simply connected domain provided by an embodiment of the present invention;

[0049] Figure 6 A side schematic diagram of the moment of inertia of a surgical tool provided by an embodiment of the present invention;

[0050] Figure 7 A schematic diagram of the traction situation at the position to be operated on provided by an embodiment of the present invention;

[0051] Figure 8 A schematic diagram of the boundary of the highest quality range and the boundary of the maximum loss quality range provided by an embodiment of the present invention. Detailed implementation manners

[0052] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a real-time magnetic resonance navigation system for neurosurgical operations proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0054] The following specifically describes the specific solution of a real-time magnetic resonance navigation system for neurosurgical operations provided by the present invention with reference to the accompanying drawings.

[0055] [[ID=4)); Please refer to Figure 1 , which shows a structural block diagram of a real-time magnetic resonance navigation system for neurosurgical operations provided by an embodiment of the present invention. The system includes: an image acquisition module 101, a dynamic area recognition module 102, a quality adjustment module 103, and a navigation execution module 104.

[0056] The image acquisition module 101 is used to obtain intraoperative continuous-frame MRI images.

[0057] In the existing real-time magnetic resonance navigation system in neurosurgery, it mainly scans before the operation by using MRI technology, then establishes the technical plan for neurosurgery according to the scanning results, and performs the operation according to the navigation parameter settings in the navigation model navigation parameters. And it uses MRI technology for real-time scanning, and combines real-time MRI images through positioning and tracking to perform magnetic resonance navigation for the operation. For the basic operation process, please refer to Figure 2 , which shows a basic operation flow chart of real-time navigation in an existing neurosurgery provided by an embodiment of the present invention.

[0058] Since the quality of the transmitted and updated scanned images will affect the accuracy of subsequent navigation planning, generally high-quality images as a whole are provided for navigation. However, the transmission of high-quality images as a whole will generate a large computational gap and affect the timeliness. To balance real-time calculation and quality, when further analyzing during tool positioning and tracking after real-time scanning, through the dynamic adjustment of regional positioning, local high-quality updates are achieved, reducing the balance problem between image quality and real-time performance caused by global high quality.

[0059] In the embodiment of the present invention, during the process of performing neurosurgery, the magnetic resonance scanner needs to perform two processes of scanning on the patient. The first scan is the pre-operative MRI scan process, and its purpose is to obtain the comprehensive condition of the patient to be operated on for formulating the treatment plan. Therefore, high-quality images as a whole are required for information collection to judge the disease focus area. Please refer to Figure 3 , which shows a schematic diagram of high-quality global MRI images provided by an embodiment of the present invention.

[0060] And further collect intraoperative continuous-frame MRI images during the operation to provide an analysis basis for real-time feedback. In neurosurgery, a magnetic resonance wireless coil is used to enhance the image quality and clarity of the original MRI images, improving the accuracy of subsequent image analysis. It should be noted that the frequency of collecting MRI images during the patient's operation can be adjusted by itself. In the embodiment of the present invention, the sampling frequency can be set to 2HZ, and the continuous-frame images within the first 2 seconds before the current moment are used as auxiliary analysis images.

[0061] The dynamic region recognition module 102 is used to determine the instrument connected region of each frame of MRI image according to the boundary regular distribution of the connected regions in each frame of MRI image; determine the operation position of each frame of MRI image by analyzing the endpoint movement degree of the instrument connected region in consecutive frames of MRI images; determine the traction influence region of the operation position according to the direction distribution of the edges in the local range at the operation position of each frame of MRI image; obtain the intraoperative dynamic region of each frame of MRI image through the proximity degree between consecutive frames of MRI images before each frame of MRI image at the operation positions.

[0062] There are operation regions and non-operation regions in the patient's MRI images. Since the actual operation region does not involve the entire scanned surgical local region, it is necessary to distinguish and determine. First, track the tool instrument part during the operation. Compared with the patient's local tissues, the surgical tool has, first of all, the texture uniformity and shape regularity. Therefore, this feature is used to track the surgical tool in the MRI image. Please refer to Figure 4 , which shows a schematic diagram of a partial surgical tool instrument provided by an embodiment of the present invention.

[0063] The possibility that the region is an instrument tool is reflected by the morphological regularity and uniformity of the region in the MRI image, so as to facilitate the subsequent determination of the operation position. First, obtain the connected regions for each frame of MRI image. It should be noted that the method for obtaining the connected regions is a well-known technical means for those skilled in the art and will not be elaborated here. Please refer to Figure 5 , which shows a schematic diagram of a single connected region provided by an embodiment of the present invention.

[0064] Then, preliminarily determine the connected region where the instrument exists according to the boundary. Preferably, in the embodiment of the present invention, the method for obtaining the instrument connected region includes:

[0065] First, for any connected region in each frame of MRI image, take any pixel point on the boundary of the connected region as the starting point, and form a sequence of pixel points continuously distributed clockwise along the boundary as the boundary sequence of the connected region. The shape complexity of the connected region is reflected by the change between consecutive points on the boundary.

[0066] Compared with the surgical tool, the brain tissue has a more complex shape. Therefore, the complexity of the direction turning is much higher than that of the surgical tool part. Therefore, the direction change of adjacent pixel points can be used to distinguish the surgical tool from the brain tissue. In the boundary sequence, when the gradient direction between each pixel point and the next pixel point is different, it indicates that the direction turns, and the corresponding pixel point is used as the turning pixel point of the connected region. The number of turning pixel points is counted and normalized to obtain the turning degree of the connected region. When the number of turning pixel points is more, it indicates that the boundary complexity is higher and it is more likely to be a tissue region.

[0067] It should be noted that normalization is a technical means well-known to those skilled in the art. The choice of normalization can be linear normalization or standard normalization, etc. The specific normalization method is not limited herein.

[0068] Finally, the connected domain with the smallest turning degree in each frame of MRI image is used as the instrument connected domain, which reflects the surgical tool part. The surgical tool part can be used to locate the region to be operated. The tool generally has a certain length, and the action point of the tool is directed at the human tissue. Therefore, based on the tool tracking, the region to be operated can be obtained through the moment of inertia of the tool. The end with a larger moment of inertia is the end far from the region to be operated, and the end with a smaller moment of inertia is the end close to the region to be operated. Please refer to Figure 6 Figure 5, which shows a side schematic view of the moment of inertia of a surgical tool provided by an embodiment of the present invention.

[0069] The position of the region to be operated is determined by the movement of the end point. In the embodiment of the present invention, the method for obtaining the position of the region to be operated includes:

[0070] First, the instrument connected domains in each frame of MRI image are linearly fitted to obtain the instrument straight line of each frame of MRI image. In the embodiment of the present invention, the linear fitting of the connected domain is performed by the least squares method, and the Hough transform can also be selected. The linear fitting process is a technical means well-known to those skilled in the art and will not be elaborated and limited herein.

[0071] For any frame of MRI image, one end point of the instrument straight line in the MRI image is sequentially used as the analysis point, the displacements between the analysis points of the instrument straight lines of each adjacent frame of MRI before the MRI image are calculated, and the mean value is calculated to obtain the moment of inertia value of the MRI image at the analysis point. The moment of inertia situation is reflected by the displacement distance changing between consecutive frames. When the moment of inertia is smaller, it indicates that it is more likely to be a point close to the tissue part.

[0072] The displacement of the tool at the position of the region to be operated is usually small, and the displacement of the tool at the non-region to be operated position is usually long. Therefore, the end point with the smallest moment of inertia value in the MRI image is used as the position of the region to be operated in the MRI image, and the real operation point is locked based on the difference in end point displacement.

[0073] The traction effect refers to that when the tool is used for the operation, because the tissue is locally connected, and the tissue related to the human nerves is generally soft, and there is only one region to be operated under the action of the surgical tool. When the region to be operated is acted upon by the surgical tool, it often has a pulling effect on the nerve tissue connected to the region to be operated. These pulled regions need to be key attention regions during the operation. Please refer to Figure 7 Figure Figure 7 , which shows a schematic diagram of the traction situation of a region to be operated provided by an embodiment of the present invention.

[0074] The tissues involved under the action of the operation point have obvious bulges at the edges and highly similar directions. Therefore, the affected area is determined by the edge distribution in the image. Preferably, in the embodiment of the present invention, the method for determining the traction influence area of the operation position includes:

[0075] First, for any operation position in an MRI image, within a preset local range at the operation position, the existing edges are recorded as the involved edges of the operation position to obtain the local existing edge situation. The edges may be generated due to tissue involvement. In the embodiment of the present invention, the preset local range is set as a circular range centered on the operation position with a radius of 6. The complete edge lines where edges are determined to exist through edge detection within the local range are used as the traction edges, and the specific numerical values can be adjusted by the implementer.

[0076] Furthermore, obtain the extension direction of each involved edge at the operation position and analyze it based on the feature of highly similar directions. In the embodiment of the present invention, each involved edge is linearly fitted, and the direction of the fitted straight line is used as the extension direction of each involved edge, reflecting the main direction vector of the edge distribution.

[0077] Furthermore, for any involved edge at the operation position, calculate the similarity mean between the extension direction of this involved edge and the adjacent involved edges to obtain the involvement significance of this involved edge. In the embodiment of the present invention, the similarity between the extension directions can be calculated by cosine similarity. If the direction similarity is high, it indicates that this edge and the adjacent edges are both likely to be part of the area where involvement occurs.

[0078] Therefore, when the involvement significance is greater than the preset significance threshold, it is considered that there are edges affected by involvement. The corresponding involved edges are used as the influence edges, and the connected regions where the image edges are located at the operation position are merged to obtain the traction influence area of the operation position. The overall part affected by the traction effect is determined by the area covered by the existing influence edges. In the embodiment of the present invention, the preset significance threshold is set to 0.85, and its numerical value can be adjusted by the implementer.

[0079] In neurosurgery, only focusing on the operation point and the traction area is very limited. It is often necessary to perform auxiliary analysis of the changes in adjacent consecutive frames to more comprehensively control the real-time state. Therefore, it is necessary to obtain a more complete dynamic area through the operation point and the traction area in time series to ensure the quality of the image around the operation point and thus ensure the efficiency of intraoperative navigation.

[0080] Preferably, in the embodiment of the present invention, the method for obtaining the intraoperative dynamic area includes:

[0081] First, for any frame of MRI image, each frame of MRI image before this MRI image is successively used as the analysis image, and each frame of image in the time series is analyzed. Calculate the Euclidean distance between the operation position in this MRI image and the analysis image as the proximity parameter of the analysis image. The closer the positions are, the greater the possibility that the operation positions in the images at this moment involve the same nerve tissue.

[0082] Furthermore, the analysis images with proximity parameters less than the preset proximity threshold are used as the approximate images of this MRI image. The approximate images reflect the other influence ranges in this area that the operation point position may involve when performing surgery in this area. In the embodiment of the present invention, the preset proximity threshold is set to 3, and the specific value can be adjusted by the implementer according to the specific implementation situation and is not limited here.

[0083] Therefore, the traction influence area in the approximate image is superimposed on the traction influence area of this MRI image to obtain the intraoperative dynamic area of this MRI image. By superimposing the areas of multiple operation positions close to the moment of this MRI image, the historical overall traction influence range existing when performing surgery near this operation position is reflected, and all need to be considered to ensure the area quality in the follow-up.

[0084] The quality adjustment module 103 is used to determine the gradient attenuation quality of the connected domains in each frame of MRI image according to the position distribution of the intraoperative dynamic area of the operation position and other connected domains in each frame of MRI image; and determine the transmission quality of each connected domain at the current moment according to the gradient attenuation quality of each connected domain except the instrument connected domain in the MRI image at the current moment in different MRI images.

[0085] The gradient quality decline is a process of radiating from the area with traction as the basis to the surrounding areas, and the image quality gradually declines during the radiation process. To ensure the high quality of the affected dynamic area, and the quality declines faster when it is farther away from the intraoperative dynamic area. Therefore, first analyze the decline situation of each frame of MRI image for the current situation.

[0086] Preferably, in the embodiment of the present invention, the method for obtaining the gradient attenuation quality includes:

[0087] For any frame of MRI image, take the operation position in this MRI image as the center of the circle, and use the farthest distance from the center of the circle to the boundary of the intraoperative dynamic area as the radius to draw a circle to determine the boundary of the highest quality range of this MRI image. This range includes the overall range of the connected domains involved in the intraoperative influence, and high-quality transmission needs to be retained to ensure the accuracy of subsequent navigation. The quality coefficient within the boundary of the highest quality range is the preset maximum quality coefficient. In the embodiment of the present invention, the preset maximum quality coefficient is 1, and the image quality is fully retained.

[0088] Further, calculate the distance between the surgical position of the MRI image and the boundary of each other connected region. Draw a circle with the length farthest from the boundary of other connected regions as the radius to determine the boundary of the maximum loss quality range of the MRI image. This range is the farthest range for adjusting the quality of the connected region in the image, that is, the boundary of the maximum range of quality loss. The quality coefficient on the boundary of the maximum loss quality range is the preset minimum quality coefficient. In the embodiment of the present invention, the preset minimum quality coefficient is 0.5, and only half of the image quality needs to be retained to reduce the data display time during transmission. Please refer to Figure 8 , which shows a schematic diagram of the highest quality range boundary and the maximum loss quality range boundary provided by an embodiment of the present invention.

[0089] Between the boundary ranges, the quality coefficient decreases gradually. Decay the quality coefficient between the highest quality range boundary and the maximum loss quality range boundary to obtain the quality coefficient at each position. In the embodiment of the present invention, the method for obtaining the quality coefficient includes:

[0090] Take the shortest connection line from the highest quality range boundary to the maximum loss quality range boundary as the gradient descent line. Starting from the quality coefficient of the highest quality region boundary, use the attenuation function along the gradient descent line to decay the quality coefficient to the quality coefficient on the maximum loss quality range boundary, and determine the quality coefficient at each position on the gradient descent line. In the embodiment of the present invention, the selected attenuation function is the negative exponential function, the starting quality coefficient is 1, and the ending quality coefficient is 0.5. As the length from the starting point becomes longer, the quality coefficient decays exponentially to 0.5. The distance length between each position on the gradient descent line and the surgical position is used as the radius, and the corresponding exponential coefficient is formed on the circular boundary.

[0091] Since the quality of the tissue region needs to be ensured to be the same, further calculate the mean value of the quality coefficients existing in each connected region in the MRI image through combination as the gradient attenuation quality of each connected region to maintain the tissue integrity criterion.

[0092] By analyzing the possible quality attenuation situations of all regions before the current moment, further determine the quality retention situation of each connected region at the final current moment. In the embodiment of the present invention, the method for obtaining the transmission quality includes:

[0093] In the MRI image at the current moment, take the maximum gradient attenuation quality of each connected region except the instrument connected region in all other MRI images as the transmission quality of each connected region at the current moment. Through analysis in different frames involved, take the maximum required gradient attenuation quality as the transmission quality for current adjustment to ensure the integrity of dynamic information.

[0094] The navigation execution module 104 is used to update the MRI image based on the transmission quality of different connected components at the current moment, and obtain the intraoperative transmission image to acquire the navigation path.

[0095] After determining the quality of different regions in the image at the current moment, the real-time scanned image is transmitted and updated and then output. In the embodiment of the present invention, the MRI image at the current moment and the transmission quality situation are used as inputs, and through the trained image transmission model, the intraoperative transmission image is output for subsequent navigation planning. In the embodiment of the present invention, the image transmission model adjusts the local image resolution of the image based on the transmission quality of different regions, retaining more regions with higher transmission quality. The model training is a well-known technical means for those skilled in the art and will not be elaborated here.

[0096] In the embodiment of the present invention, first, the preoperatively acquired MRI image is registered with the intraoperative transmission image to ensure that the intraoperative transmission image is consistent with the actual position of the head. Secondly, tool tracking is performed through the real-time transmitted intraoperative transmission image, and then the route marking is completed by using the neurosurgery implementation plan established based on the MRI image preoperatively. Then, positioning is performed using the tracked surgical tool according to the marked route, and the navigation path is displayed to complete the navigation.

[0097] The present invention determines the surgical tool part through the boundary rule distribution of the connected component and the movement situation of consecutive frames, and then tracks and locates the affected position. Subsequently, it is convenient to analyze the affected area at the affected position to ensure high-quality imaging of the local image. Further, it is ensured that the areas affected by tissue traction during the operation can also be observed and analyzed. According to the local deformation edge that will occur at the affected position during the operation, the range of the traction area affected by the operation is determined through the local tissue edge distribution at the affected position, and considering the surgical scope situations involved many times in the previous steps, the intraoperative dynamic area affected during the operation is determined according to the distribution proximity of the affected position, making the key attention area in each frame of the image more accurate. Then, through the gradient attenuation analysis of the distribution of the intraoperative dynamic area that is the key attention in each frame of the image and the remaining areas, the attenuation quality situation is obtained, and considering the integrity and consistency of the regional tissue during the operation, the transmission quality of the final connected area is determined through the gradient attenuation quality situations in different image frames before the current moment for transmission display and navigation. The present invention locates the affected area during the operation after tracking the instrument area, dynamically adjusts the quality of different regions, and balances the transmission quality and real-time issues.

[0098] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized respectively.

Claims

1. A real-time magnetic resonance navigation system for neurosurgery, characterized in that, The system includes: an image acquisition module for acquiring intraoperative continuous-frame MRI images; a dynamic region recognition module for determining the instrument connected region of each frame of MRI image according to the boundary regular distribution of connected regions in each frame of MRI image; determining the operation position of each frame of MRI image by analyzing the endpoint movement degree of the instrument connected region in the continuous-frame MRI images; determining the traction influence region of the operation position according to the direction distribution of edges in the local range at the operation position of each frame of MRI image; obtaining the intraoperative dynamic region of each frame of MRI image through the proximity degree between the continuous-frame MRI images before each frame of MRI image at the operation positions; a quality adjustment module for determining the gradient attenuation quality of the connected regions in each frame of MRI image according to the position distribution of the intraoperative dynamic region of the operation position and other connected regions in each frame of MRI image; determining the transmission quality of each connected region at the current moment according to the gradient attenuation quality of each connected region except the instrument connected region in the MRI images at different times; a navigation execution module for updating the MRI image based on the transmission quality of different connected regions at the current moment to obtain an intraoperative transmission image and acquire a navigation path.

2. The real-time magnetic resonance navigation system for neurosurgery according to claim 1, wherein The method for obtaining the instrument connected region includes: For any connected region in each frame of MRI image, taking any pixel point on the boundary of the connected region as a starting point, and forming a sequence of pixel points continuously distributed clockwise along the boundary as the boundary sequence of the connected region; In the boundary sequence, when the gradient direction between each pixel point and the next pixel point is different, the corresponding pixel point is used as the turning pixel point of the connected region; counting the number of turning pixel points and performing normalization processing to obtain the turning degree of the connected region; Taking the connected region with the smallest turning degree in each frame of MRI image as the instrument connected region.

3. The real-time magnetic resonance navigation system for neurosurgery according to claim 1, characterized in that, The method for obtaining the operation position includes: Performing linear fitting on the instrument connected regions in each frame of MRI image to obtain the instrument line of each frame of MRI image; For any frame of MRI image, sequentially taking one endpoint of the instrument line in the MRI image as an analysis point; calculating the displacement between the instrument lines of adjacent frames of MRI before the MRI image at the analysis point and calculating the mean value to obtain the moment of inertia value of the MRI image at the analysis point; Taking the endpoint with the smallest moment of inertia value in the MRI image as the operation position of the MRI image.

4. The real-time magnetic resonance navigation system for neurosurgery according to claim 1, wherein, The method for obtaining the traction influence region includes: For the operation position of any MRI image, within the preset local range at the operation position, marking the existing edges as the involved edges of the operation position; obtaining the extension direction of each involved edge of the operation position; For any involved edge of the operation position, calculating the similarity mean value between the involved edge and the adjacent involved edges in the extension direction to obtain the involvement significance of the involved edge; When the involvement significance is greater than the preset significance threshold, taking the corresponding involved edge as the influencing edge; merging the connected regions where the image edges are located at the operation position to obtain the traction influence region of the operation position.

5. The real-time magnetic resonance navigation system for neurosurgery according to claim 1, characterized in that, The method for obtaining the intraoperative dynamic region includes: For any frame of MRI image, each frame of MRI image before this MRI image is sequentially used as the analysis image; Calculate the Euclidean distance between the surgical positions of this MRI image and the analysis image as the proximity parameter of the analysis image; The analysis image with the proximity parameter less than the preset proximity threshold is used as the approximate image of this MRI image; Overlay the traction influence area in the approximate image and the traction influence area of this MRI image to obtain the intraoperative dynamic area of this MRI image.

6. The real-time magnetic resonance navigation system for neurosurgery according to claim 1, characterized in that, The method for obtaining the gradient attenuation quality includes: For any frame of MRI image, take the surgical position in this MRI image as the center of the circle, and use the farthest distance from the center of the circle to the boundary of the intraoperative dynamic area as the radius to draw a circle to determine the boundary of the highest quality range of this MRI image; The quality coefficient within the boundary of the highest quality range is the preset maximum quality coefficient; Calculate the distance between the surgical position of this MRI image and the boundary of each other connected domain, and use the length farthest from the boundary of other connected domains as the radius to draw a circle to determine the boundary of the maximum loss quality range of this MRI image; The quality coefficient on the boundary of the maximum loss quality range is the preset minimum quality coefficient; Attenuate the quality coefficient between the boundary of the highest quality range and the boundary of the maximum loss quality range to obtain the quality coefficient at each position; Calculate the mean value of the quality coefficients existing in each connected domain in this MRI image as the gradient attenuation quality of each connected domain.

7. The real-time magnetic resonance navigation system for neurosurgery according to claim 6, wherein The attenuating the quality coefficient between the boundary of the highest quality range and the boundary of the maximum loss quality range to obtain the quality coefficient at each position includes: Take the shortest connection line from the boundary of the highest quality range to the boundary of the maximum loss quality range as the gradient descent line; Starting from the quality coefficient of the boundary of the highest quality area, use the attenuation function to attenuate the quality coefficient along the gradient descent line to the quality coefficient on the boundary of the maximum loss quality range, and determine the quality coefficient at each position on the gradient descent line.

8. The real-time magnetic resonance navigation system for neurosurgery according to claim 1, characterized in that, The method for obtaining the transmission quality includes: In the MRI image at the current moment, use the maximum gradient attenuation quality of each connected domain except the instrument connected domain in all other MRI images as the transmission quality of each connected domain at the current moment.

9. The real-time magnetic resonance navigation system for neurosurgery according to claim 1, wherein The method for obtaining the intraoperative transmission image includes: Use the MR image and the transmission quality situation at the current moment as the input, and output the intraoperative transmission image through the trained image transmission model.

10. The real-time magnetic resonance navigation system for neurosurgery according to claim 4, wherein The method for obtaining the extension direction of each traction edge at the surgical position includes: Perform linear fitting on each traction edge, and use the direction of the fitted straight line as the extension direction of each traction edge.

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