Intraoperative ultrasound system for glioblastoma

Abstract

This application relates to the field of medical system technology, and in particular to an intraoperative ultrasound examination system for glioblastoma. This application achieves full-process quantitative risk warning and path optimization for glioblastoma resection by combining preoperative multimodal image fusion with intraoperative real-time ultrasound monitoring, dynamic safety distance adjustment and abnormal operation impact analysis. Its core advantage lies in the deep integration of static surgical planning with dynamic factors such as intraoperative brain tissue displacement, edema progression, and instrument deviation. Through adaptive safety threshold calculation and real-time path replanning, it significantly improves the protection accuracy of key structures such as motor, language function areas and blood vessels while ensuring maximum tumor resection.

Description

Technical Field

[0001] This application relates to the field of medical system technology, and in particular to an intraoperative ultrasound examination system for glioblastoma. Background Technology

[0002] Glioblastoma is the most common and most aggressive primary malignant brain tumor in adults. The gold standard for its treatment is to achieve the maximum possible safe resection while preserving neurological function. Numerous clinical studies have confirmed that the extent of resection is significantly correlated with progression-free survival and overall survival. However, this seemingly clear goal faces a fundamental and dynamic challenge in surgical practice: how to precisely locate and completely remove the tumor in the constantly changing, fragile, and functionally dense brain tissue, while flawlessly avoiding critical neural structures and blood vessels that determine the patient's postoperative quality of life. Current technologies exhibit significant and interconnected limitations in addressing the dynamic nature of surgery: Preoperative neuronavigation systems based on high-resolution multimodal magnetic resonance imaging (MRI, including structural imaging, diffusion tensor imaging (DTI), and functional MRI) are the cornerstone of surgical planning. They can reconstruct tumor boundaries, cortical functional areas, subcortical white matter fiber tracts, and important blood vessels in three dimensions, providing valuable spatial relationship information. However, once the skull is opened, cerebrospinal fluid is released, part of the tumor is removed, or dehydrating drugs are used, brain tissue inevitably undergoes non-linear displacement and deformation, i.e., brain displacement. At this point, the correspondence between preoperative images and the actual intraoperative anatomical location is broken, and the map displayed by the navigation system becomes distorted. Continuing to rely on this outdated information for depth or boundary manipulation can easily lead to accidental damage to functional areas or blood vessels, or residual tumor. The reliability of static navigation decreases sharply in the mid-to-late stages of surgery.

[0003] Therefore, there is a need for an intraoperative ultrasound examination system for glioblastoma that combines preoperative multimodal image fusion with intraoperative real-time ultrasound monitoring to plan the cutting path in real time. Summary of the Invention

[0004] To overcome the defects and shortcomings of existing technologies, this application provides an intraoperative ultrasound examination system for glioblastoma. This solution achieves full-process quantitative risk warning and path optimization for glioblastoma resection by combining preoperative multimodal image fusion with intraoperative real-time ultrasound monitoring, dynamic safety distance adjustment, and abnormal analysis of operational influences. Its core advantage lies in the deep integration of static surgical planning with dynamic factors such as intraoperative brain tissue displacement, edema progression, and instrument deviation. Through adaptive safety threshold calculation and real-time path replanning, it significantly improves the accuracy of protection for key structures such as motor, language function areas, and blood vessels while ensuring maximum tumor resection.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides an intraoperative ultrasound examination system for glioblastoma, comprising the following modules: The surgical procedure includes a preoperative data acquisition module, a surgical process monitoring module, a surgical operation impact analysis module, a surgical process early warning module, an intraoperative optimization module, and an intraoperative display module. Specifically, the preoperative data acquisition module acquires information about the brain near the tumor location and marks key structures; the surgical process monitoring module continuously tracks the spatial position and posture of the patient's head and surgical instruments, and calibrates the marked positions of key structures; the surgical operation impact analysis module analyzes the impact of the operation on the tumor's position and morphology, the response of the peritumoral environment, and the vibration of the surgical instruments during the cutting process; the surgical process early warning module assesses and issues warnings regarding the safety of the surgical path based on the results of the operation impact analysis; the intraoperative optimization module selects the surgical path based on the safety assessment results; and the intraoperative display module displays the selected surgical path.

[0006] In one implementation of this application, the acquisition of brain conditions near the tumor location and the annotation of key structures include the following specific content: importing the patient's preoperative MRI (T1-enhanced, T2-FLAIR, DWI, etc.), fMRI (functional magnetic resonance imaging), and DTI (diffusion tensor imaging) data, aligning the above images with the intraoperative ultrasound coordinate system with high precision to form a fused image; and using an image processing module to delineate and mark key structures in the tumor area, motor function area, language function area, and arteriovenous vascular area.

[0007] In one implementation of this application, the calibration of the key structural annotation positions includes the following specific steps: The first step is to track the spatial coordinates and posture of the patient's head, ultrasound probe, and key surgical instruments (such as suction device and bipolar electrocoagulation device) in real time during the operation. It should be noted that a three-dimensional rectangular coordinate system is constructed by using any object in the operating room that will not move during the operation as the reference origin, preferably any contact position between the operating table and the patient's head at the beginning of the operation. The exposed skull landmarks or brain surface features are obtained, and the preoperative fused images are aligned with the actual position of the patient's head in real time. At the same time, the movement trajectory of the patient's head and key surgical instruments is obtained. The second step involves continuously comparing intraoperative real-time ultrasound images with preoperative MRI images to detect brain tissue displacement caused by dehydration and tumor resection, and to correct the position of key structures marked preoperatively (especially tumor boundaries and deep fiber bundles) in the navigation system in real time.

[0008] In one implementation of this application, the operational impact analysis specifically includes the following: The first step involves real-time acquisition of the tumor's echogenicity and boundary changes in ultrasound images during the resection process. This monitors the tumor's shrinkage, the location and morphology of the remaining tumor, and the average speed and direction of movement of the remaining tumor towards key structures during the cutting process. The real-time distance from the remaining tumor to the key structures in the corresponding direction of movement is also obtained. Abnormal cutting speeds are identified by dividing the average speed by the safe speed. By tracking changes in the tumor's echogenicity boundary in real time, the process of tumor shrinkage can be visualized, and the displacement trajectory of the remaining tumor can be precisely captured. The average speed and direction of movement towards key structures (such as the motor cortex, language area, and major blood vessels) can be calculated and compared with preset safe speed thresholds. This allows for immediate assessment of the risk of abnormal tumor movement due to traction, cerebrospinal fluid loss, or brain tissue displacement. During surgery, unpredictable deformation and displacement of brain tissue can occur due to tumor resection, cerebrospinal fluid drainage, gravity, and other factors, posing one of the biggest challenges to precise resection. Ultrasound is the only technology capable of penetrating the skull and providing real-time intraoperative imaging. The second step involves obtaining the edema status of the corresponding tissues at the key structures, including edema volume, rate of volume change, edema depth, and velocity of depth change. Here, the velocity of depth change represents the speed of movement outwards from the tissue; movement inwards is marked as negative. Abnormalities in the corresponding parameters are obtained by dividing the edema volume, rate of volume change, edema depth, and velocity of depth change by their respective safety values. The weighted sum of these abnormalities yields the edema anomaly. This step quantifies the edema volume, rate of volume change, edema depth, and velocity of depth change. The four dimensions of depth and rate of change enable objective and continuous monitoring of edema progression. By normalizing each parameter to outliers and weighted summing, the overall threat level of edema to key structures can be comprehensively reflected. Cerebral edema usually appears as a hypoechoic (dark) halo around the lesion on ultrasound images. The contour of this hypoechoic region can be displayed by image segmentation algorithms (such as region growing or machine learning). This is a conventional technique and will not be elaborated on here. The volume and maximum depth (distance of invasion into key structures) can be calculated. The volume / depth difference at consecutive time points is the rate of change. The third step is to obtain the planned instrument trajectory and the actual instrument trajectory during automatic operation. The average distance between the actual instrument trajectory and the planned instrument trajectory at each point is obtained. The average distance between the actual instrument trajectory and the planned instrument trajectory at each point is divided by the allowable error of instrument operation to obtain the abnormality of instrument operation. By comparing the actual trajectory of the instrument with the planned ideal trajectory in real time and calculating the average deviation, execution deviations caused by mechanical errors of the instrument, inaccurate brain tissue resistance feedback, or registration drift can be immediately detected. The fourth step involves obtaining abnormal edema, abnormal cutting and movement speed, and abnormal instrument operation at the corresponding locations of key structures. These abnormalities are multiplied by their respective influence coefficients and then summed to obtain the operational impact abnormalities. By monitoring the tumor resection speed, changes in edema around key structures, and the accuracy of surgical instrument trajectory in real time, and by integrating these three types of abnormal data for comprehensive risk assessment, it can identify potential risks caused by tissue displacement, acute edema, or instrument deviation in advance.

[0009] In one implementation of this application, the step of determining and issuing early warnings for surgical pathway safety based on the results of operational impact analysis includes the following specific aspects: The first step is to obtain the real-time distance between the residual tumor and the critical structure in the corresponding direction of movement, as well as the safety distance set before the operation. For example, the doctor sets the safety boundary of the operation based on the distance between the tumor and the critical structure. The second step is to sum the abnormal operation impact with the value 1 and then multiply it by the pre-set safe distance to obtain the actual safe distance. The actual safe distance is then compared with the real-time distance of the critical structure in the corresponding direction of movement. If the actual safe distance is greater than or equal to the real-time distance of the critical structure in the corresponding direction of movement, it indicates that the original surgical path is unsafe and an early warning for the original surgical path is issued. If the actual safe distance is less than the real-time distance of the critical structure in the corresponding direction of movement, it indicates that the original surgical path is safe and no early warning for the original surgical path is issued. The benefits of this step are as follows: by introducing abnormal factors affecting the operation to dynamically correct the pre-set safety boundary, the actual safety distance is calculated in real time and compared with the real-time distance between the surgical instruments and key structures. Once the real-time distance is less than the dynamically adjusted safety threshold, the system immediately triggers an early warning, prompting the doctor to pause or adjust the operation. This mechanism combines static planning with dynamic changes during the operation, realizing the quantitative assessment and pre-intervention of surgical risks, and providing continuous and adaptive safety monitoring for delicate surgeries such as tumor resection in the core functional area.

[0010] In one implementation of this application, the step of selecting the surgical path based on the safety assessment result includes the following specific details: The process involves obtaining information on potential surgical paths and selecting a path where all points on the path are at least equal to the actual safe distance from the critical structure as a secondary alternative. If no such path exists, the process involves obtaining the number of neurons and blood flow at the intersection of the candidate and selected surgical paths within the actual safe distance range with the critical structure region. The standardized number of neurons and blood flow are obtained by dividing these values ​​by the corresponding safe values. The weighted sum of these values ​​yields the path's risk value, and the surgical path with the lowest risk value at the intersection is selected as the secondary alternative.

[0011] In one implementation of this application, displaying the selected surgical path includes the following specific details: During the surgery, secondary alternative paths are obtained in real time based on changes in the surgical situation. These alternative paths are then imported into the surgical equipment and displayed on the real-time surgical screen. The surgical equipment operates according to the real-time alternative paths.

[0012] Secondly, this application also provides a method for intraoperative ultrasound examination of glioblastoma, including the following specific steps: Obtain information about the brain near the tumor location and annotate key structures; Continuously track the spatial position and posture of the patient's head and surgical instruments, and calibrate the marked positions of key structures; The operational impact was analyzed based on the tumor's location and morphological changes, the response of the surrounding environment, and the vibration of the surgical instruments during the cutting process. Based on the results of the operational impact analysis, assess the safety of the surgical pathway and issue early warnings. The surgical approach is selected based on the safety assessment results. Used to display the selected surgical path.

[0013] Thirdly, this application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an intraoperative ultrasound examination method for glioblastoma by calling the computer program stored in the memory.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an intraoperative ultrasound examination method for glioblastoma.

[0015] Compared with the prior art, this application has the following advantages and beneficial effects: By combining preoperative multimodal image fusion and intraoperative real-time ultrasound monitoring with dynamic safety distance adjustment and abnormal analysis of operational impact, a full-process quantitative risk warning and path optimization for glioblastoma resection is achieved. Its core advantage lies in the deep integration of static surgical planning with dynamic factors such as intraoperative brain tissue displacement, edema progression, and instrument deviation. Through adaptive safety threshold calculation and real-time path replanning, the precision of protection for key structures such as motor, language function areas, and blood vessels is significantly improved while ensuring maximum tumor resection. Attached Figure Description

[0016] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process structure of an embodiment of the method of this application; Figure 2 This is a schematic diagram of the steps involved in the operational impact anomaly analysis of the method embodiment of this application; Figure 3 This is a schematic diagram of the module composition structure of an embodiment of the system in this application. Detailed Implementation

[0017] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0018] First, it is necessary to explain the scenario to be addressed in this embodiment. For industrial or office areas, there is a need to select a location that is convenient for employees to exercise during their spare time. The selection and planning of exercise facilities should be carried out, and the optimal exercise facilities that are suitable for the terrain, the parts of the employee's body that need to be exercised, and safety should be selected from the existing exercise facility library for installation. Please see Figures 1 to 2 , Figure 1 This is a schematic diagram of the overall process of the intraoperative ultrasound examination method for glioblastoma provided in the embodiments of this application, which specifically includes the following steps: S1. Obtain information about the brain near the tumor location and annotate key structures; In this embodiment, obtaining information about the brain near the tumor location and annotating key structures includes the following: importing the patient's preoperative MRI (T1-enhanced, T2-FLAIR, DWI, etc.), fMRI (functional magnetic resonance imaging), and DTI (diffusion tensor imaging) data; aligning the above images with the intraoperative ultrasound coordinate system with high precision to form a fused image; and using the image processing module to delineate and mark key structures in the tumor region, motor function area, language function area, and arteriovenous vascular region. S2. Continuously track the spatial position and posture of the patient's head and surgical instruments, and calibrate the marked positions of key structures; In this embodiment, the calibration of the marked positions of key structures includes the following specific steps: The first step is to track the spatial coordinates and posture of the patient's head, ultrasound probe, and key surgical instruments (such as suction device and bipolar electrocoagulation device) in real time during the operation. It should be noted that a three-dimensional rectangular coordinate system is constructed by using any object in the operating room that will not move during the operation as the reference origin, preferably any contact position between the operating table and the patient's head at the beginning of the operation. The exposed skull landmarks or brain surface features are obtained, and the preoperative fused images are aligned with the actual position of the patient's head in real time. At the same time, the movement trajectory of the patient's head and key surgical instruments is obtained. The second step involves continuously comparing intraoperative real-time ultrasound images with preoperative MRI images to detect brain tissue displacement caused by dehydration and tumor resection, and to correct the position of key structures marked preoperatively (especially tumor boundaries and deep fiber bundles) in the navigation system in real time. S3. Analyze the impact of the operation on the location and morphological changes of the tumor, the response of the surrounding environment, and the vibration of the surgical instrument trajectory during the cutting process. In this embodiment, the operational impact analysis specifically includes the following: The first step involves real-time acquisition of ultrasound images during the resection process, monitoring changes in tumor echogenicity and boundaries, tracking tumor volume reduction, and observing the location and morphology of residual tumors. It also involves acquiring the average velocity and corresponding direction of movement of the residual tumor towards key structures during the cutting process, and obtaining the real-time distance from the residual tumor to the key structures in the corresponding direction of movement. Abnormal cutting speeds are identified by dividing the average velocity by the safe speed. By tracking changes in tumor echogenicity and boundaries in real time, the process of tumor volume reduction can be visualized, and the displacement trajectory of residual tumors can be accurately captured. The average velocity and direction of movement towards key structures (such as the motor cortex, speech area, and major blood vessels) can be calculated and compared with preset safe speed thresholds. Real-time monitoring of the risk of abnormal tumor movement due to traction, cerebrospinal fluid loss, or brain tissue displacement is crucial. During surgery, unpredictable deformation and displacement of brain tissue due to tumor resection, cerebrospinal fluid drainage, and gravity pose one of the biggest challenges to precise resection. Ultrasound is the only technology capable of penetrating the skull and providing real-time intraoperative imaging. Through continuous frame image analysis (such as optical flow or image registration algorithms), pixel displacement of the tumor boundary can be quantified, allowing for the calculation of its movement speed. Combining speed and direction with the anatomical location of key structures (obtained from preoperative MRI / intraoperative navigation registration) enables the creation of a dynamic spatial threat model. The introduction of moving average speed (e.g., the average over the past 5 seconds) filters out transient jitter, making warnings more stable and reliable. Safe speed thresholds are set based on clinical consensus and historical data; for example, the speed of movement towards the brainstem should be significantly lower than the speed of movement towards non-functional areas. The second step involves obtaining information on edema at the corresponding locations of key structures, including edema volume, rate of volume change, edema depth, and velocity of depth change. The velocity of depth change represents the speed of movement outwards from the tissue; movement inwards is marked as negative. Abnormalities in each parameter are obtained by dividing the edema volume, rate of volume change, edema depth, and velocity of depth change by their respective safety values. The weighted sum of these abnormalities yields the edema anomaly. This step, by quantifying the four dimensions of edema volume, rate of volume change, edema depth, and velocity of depth change, achieves objective and continuous monitoring of edema progression. Normalizing each parameter to outliers and then weighted summing them comprehensively reflects the overall threat level of edema to key structures. Cerebral edema is also observed in ultrasound images. The image typically appears as a hypoechoic (dark) halo surrounding the lesion. Image segmentation algorithms (such as region growing or machine learning) can reveal the contour of this hypoechoic region, which is a conventional technique and will not be elaborated on here. The volume and maximum depth (distance of invasion into key structures) are then calculated. The difference between volume and depth at consecutive time points is the rate of change. The harm of edema invading into key structures (increased depth) is far greater than that of edema growing outward. Therefore, the rate of change is given directionality (invasion is negative, but the larger the absolute value, the higher the risk). The safety values ​​of each parameter are set based on the tolerance of normal tissue in the area and clinical studies (such as volume growth not exceeding a certain threshold per hour). When weighted summing, the weight of the rate of change in depth is usually the highest because it is directly related to irreversible nerve compression damage. The third step involves acquiring the planned instrument trajectory and the actual trajectory during automated operation. The average distance between the actual and planned instrument trajectories at each point is calculated. Dividing this average distance by the allowable error margin for instrument operation reveals any operational anomalies. By comparing the actual trajectory with the planned ideal trajectory in real time and calculating the average deviation, execution deviations caused by mechanical errors, inaccurate brain tissue resistance feedback, or registration drift can be immediately identified. Comparing the average deviation with the allowable engineering error provides a direct indicator of instrument operational anomalies and reliability. The planned trajectory, derived from the fusion of preoperative / intraoperative images and the surgical plan, represents the ideal path. The actual trajectory is acquired in real time using the instrument's built-in position sensors (such as optical tracking, electromagnetic sensors, or encoders). Calculating the Euclidean distance between corresponding points and averaging the values ​​is the standard method for measuring overall tracking accuracy. The fourth step involves obtaining abnormal edema, abnormal cutting and movement speed, and abnormal instrument operation at the corresponding locations of key structures. These abnormalities are multiplied by their respective influence coefficients and then summed to obtain the operational impact abnormalities. By real-time monitoring of tumor resection speed, changes in edema around key structures, and the accuracy of surgical instrument trajectory, and by integrating these three types of abnormal data for comprehensive risk assessment, it can identify potential risks caused by tissue displacement, acute edema, or instrument deviation in advance. S4. Based on the results of the operational impact analysis, make safety judgments and provide early warnings for surgical pathways; In this embodiment, surgical path safety is assessed and warnings are issued based on the results of the operational impact analysis, including the following specific aspects: The first step is to obtain the real-time distance between the residual tumor and the key structures in the corresponding direction of movement, as well as the safe distance set before surgery. For example, based on the distance between the tumor and the key structures, the doctor sets the safe boundary of the surgery (such as 2mm from the corticospinal tract). The second step is to sum the abnormal operation impact with the value 1 and then multiply it by the pre-set safe distance to obtain the actual safe distance. The actual safe distance is then compared with the real-time distance of the critical structure in the corresponding direction of movement. If the actual safe distance is greater than or equal to the real-time distance of the critical structure in the corresponding direction of movement, it indicates that the original surgical path is unsafe and an early warning for the original surgical path is issued. If the actual safe distance is less than the real-time distance of the critical structure in the corresponding direction of movement, it indicates that the original surgical path is safe and no early warning for the original surgical path is issued. The benefits of this step are as follows: by introducing abnormal factors affecting the operation to dynamically correct the pre-set safety boundary, the actual safety distance is calculated in real time and compared with the real-time distance between the surgical instruments and the key structures. Once the real-time distance is less than the dynamically adjusted safety threshold, the system immediately triggers an early warning, prompting the doctor to pause or adjust the operation. This mechanism combines static planning with dynamic changes during the operation, realizing the quantitative assessment and pre-intervention of surgical risks, and providing continuous and adaptive safety monitoring for delicate surgeries such as tumor resection in the core functional area. S5. Select the surgical path based on the safety assessment results of the surgical path; In this embodiment, the selection of the surgical path based on the safety assessment result includes the following specific aspects: The process involves obtaining information on potential surgical paths and selecting a path where the distance from all points to the critical structure is greater than or equal to the actual safe distance as a secondary alternative. If no such path exists, the process involves obtaining the number of neurons and blood flow at the intersection of the candidate and selected surgical paths with the critical structure region within the actual safe distance range. The standardized number of neurons and blood flow are obtained by dividing these values ​​by the corresponding safe values. The weighted sum of these values ​​yields the path's risk value, and the surgical path with the lowest risk value at the intersection is selected as the secondary alternative. S6 is used to display the selected surgical path; In this embodiment, the selected surgical path is shown in the following specific ways: During the surgery, secondary alternative paths are obtained in real time according to changes in the surgical situation. These secondary alternative paths are then imported into the surgical equipment and displayed on the real-time surgical screen. The surgical equipment operates according to the real-time secondary alternative paths. It should be noted that in this embodiment, the weighting coefficients (such as the weights for abnormal edema, abnormal cutting and movement speed, and abnormal instrument operation, as well as the weights for neuron number and blood flow) are determined using a dynamic optimization method combining the analytic hierarchy process (AHP) based on clinical data and expert consensus with machine learning. Specifically, firstly, correlation analysis is performed on multicenter retrospective surgical data (including postoperative neurological deficits, tumor residual rate, intraoperative complications, and other outcome indicators) with intraoperative multimodal parameters (ultrasound, navigation, electrophysiology) to initially establish a ranking of the impact of each parameter on surgical safety. Subsequently, a Delphi group composed of senior neurosurgical experts is invited to conduct pairwise comparisons of the importance of each parameter based on clinical experience, constructing a judgment matrix, and calculating the initial weights after passing a consistency test. In practical applications, the system continuously collects real-time data and postoperative results for each surgery, and dynamically fine-tunes the weights using a reinforcement learning framework, enabling the model to adapt to the characteristics of different hospitals, doctors, and patient subgroups. The advantages of this method are: it respects the experience and knowledge of clinical experts, ensuring the clinical interpretability and safety of the weights, while also enabling the system to self-optimize through a data-driven approach, avoiding insufficient adaptability caused by fixed weights. In this embodiment, the setting of various safety threshold parameters adopts a method combining large-scale clinical literature meta-analysis and individualized physiological modeling. For example, the safe velocity threshold for tumor movement into the motor cortex is determined by analyzing the correlation between tumor displacement rate in historical surgical images and postoperative motor dysfunction, using receiver operating characteristic (ROC) curves to determine the optimal critical value. The safe distance not only references anatomical standards (such as the anatomical buffer zone of white matter fiber bundles) but also incorporates positive response distance data from intraoperative electrophysiological stimulation. For the safe volume / depth change rate of edema, the tolerable edema growth per unit time in different brain regions is calculated. The advantage of this method is that it transcends traditional, uniform empirical values ​​(such as a 5 mm safety boundary), achieving individualized and dynamic parameter setting. For example, for young patients with significant brain atrophy, whose brain tissue has greater compensatory space, the system can automatically increase the safe distance threshold; while for patients with existing functional area edema, a more conservative velocity threshold is used. In one specific embodiment, the patient was Mr. Li, a 52-year-old male; the diagnosis was glioblastoma of the left frontal lobe (IDH wild type); the tumor location was adjacent to the precentral gyrus (primary motor cortex); the surgical procedure was neuronavigation combined with intraoperative ultrasound-guided tumor resection; the preoperative condition was a Karnofsky score of 80 with mild left limb weakness (grade 5). Data from 156 glioma surgeries performed in similar locations at our hospital over the past three years were retrieved. The following parameter correlations were found: when the tumor displacement velocity was greater than 0.3 mm / min, the risk of motor impairment increased by 3.2 times; when the edema volume increase rate was greater than 2.5 ml / min, the risk of brain herniation increased by 4.1 times; and when the distance between the device and the functional cortex was less than 3 mm, the risk of permanent nerve damage increased by 5.7 times. Five senior neurosurgical experts independently scored the data; a judgment matrix was constructed: edema abnormality vs. velocity abnormality: 1 / 2 (edema is half the importance of velocity); edema abnormality vs. instrument abnormality: 1 / 3 (edema is one-third the importance of instrument abnormality); velocity abnormality vs. instrument abnormality: 1 / 2 (velocity is half the importance of instrument abnormality); consistency test: CR=0.03<0.1 (passed); initial weights were calculated: instrument abnormality=0.54, velocity abnormality=0.30, edema abnormality=0.16; Considering patient specificity: age of 52 years resulted in a brain atrophy score of +0.2; preoperative mild functional impairment resulted in a risk tolerance of -0.15; and rich tumor blood supply (confirmed by DSA) resulted in a bleeding risk of +0.25. The final weights are: instrument abnormality = 0.58, speed abnormality = 0.28, edema abnormality = 0.14; The advantages of the above embodiments are as follows: by combining preoperative multimodal image fusion and intraoperative real-time ultrasound monitoring with dynamic safety distance adjustment and abnormal analysis of operational influence, the whole-process quantitative risk warning and path optimization of glioblastoma resection are realized. Its core advantage lies in the deep integration of static surgical planning with dynamic factors such as intraoperative brain tissue displacement, edema progression, and instrument deviation. Through adaptive safety threshold calculation and real-time path replanning, while ensuring the maximum resection of the tumor, the protection accuracy of key structures such as motor, language function areas and blood vessels is significantly improved. Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the intraoperative ultrasound examination system for glioblastoma provided in this application embodiment, including: The surgical procedure includes a preoperative data acquisition module, a surgical process monitoring module, a surgical operation impact analysis module, a surgical process early warning module, an intraoperative optimization module, and an intraoperative display module. Specifically, the preoperative data acquisition module acquires information about the brain near the tumor location and marks key structures; the surgical process monitoring module continuously tracks the spatial position and posture of the patient's head and surgical instruments, and calibrates the marked positions of key structures; the surgical operation impact analysis module analyzes the impact of the operation on the tumor's position and morphological changes, the response of the peritumoral environment, and the vibration of the surgical instruments during the cutting process; the surgical process early warning module assesses and issues warnings regarding the safety of the surgical path based on the results of the operation impact analysis; the intraoperative optimization module selects the surgical path based on the safety assessment results; and the intraoperative display module displays the selected surgical path.

[0019] The parameters and steps for each unit module to achieve their respective functions in the intraoperative ultrasound examination system for glioblastoma described above can be found in the embodiments of the intraoperative ultrasound examination method for glioblastoma described above, and will not be repeated here.

[0020] Embodiments of this application also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a method for intraoperative ultrasound examination of glioblastoma as provided in the above embodiments, which can be loaded by the processor and executed.

[0021] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the intraoperative ultrasound examination method for glioblastoma provided in the above embodiments, etc. The data storage area may store data involved in the intraoperative ultrasound examination method for glioblastoma provided in the above embodiments, etc.

[0022] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of a specific application-specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field-programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit this.

[0023] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI bus or an EISA bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0024] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the intraoperative ultrasound examination method for glioblastoma as provided in the above embodiments.

[0025] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0026] The term includes, or any other variation thereof, is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0027] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. An intraoperative ultrasound examination system for glioblastoma, characterized in that, Includes the following modules: The surgical procedure includes a preoperative data acquisition module, a surgical process monitoring module, a surgical operation impact analysis module, a surgical process early warning module, an intraoperative optimization module, and an intraoperative display module. Specifically, the preoperative data acquisition module acquires information about the brain near the tumor location and marks key structures; the surgical process monitoring module continuously tracks the spatial position and posture of the patient's head and surgical instruments, and calibrates the marked positions of key structures; the surgical operation impact analysis module analyzes the impact of the operation on the tumor's position and morphology, the response of the peritumoral environment, and the vibration of the surgical instruments during the cutting process; the surgical process early warning module assesses and issues warnings regarding the safety of the surgical path based on the results of the operation impact analysis; the intraoperative optimization module selects the surgical path based on the safety assessment results; and the intraoperative display module displays the selected surgical path.

2. The intraoperative ultrasound examination system for glioblastoma according to claim 1, characterized in that, The process of obtaining information about the brain near the tumor location and annotating key structures includes the following: importing the patient's preoperative MRI, fMRI, and DTI data, aligning the images with the intraoperative ultrasound coordinate system with high precision to form a fused image; and using the image processing module to delineate and mark key structures in the tumor area, motor function area, language function area, and arteriovenous vascular area.

3. The intraoperative ultrasound examination system for glioblastoma according to claim 1, characterized in that, The calibration of the key structural annotation positions includes the following specific steps: The first step is to track the spatial coordinates and posture of the patient's head, ultrasound probe, and key surgical instruments in real time during the operation; The second step involves continuously comparing intraoperative real-time ultrasound images with preoperative MRI images to detect brain tissue displacement caused by dehydration and tumor resection, and to correct the position of key structures marked preoperatively in the navigation system in real time.

4. The intraoperative ultrasound examination system for glioblastoma according to claim 1, characterized in that, The operational impact analysis specifically includes the following: The first step is to acquire the echo and boundary changes of the tumor in the ultrasound images during the resection process in real time, monitor the shrinkage process of the tumor volume, the position and shape of the residual tumor, acquire the average speed and corresponding direction of movement of the residual tumor towards the key structure during the cutting path, acquire the real-time distance of the residual tumor to the key structure in the corresponding direction of movement, and obtain the abnormal cutting speed by dividing the average speed by the safe speed. The second step is to obtain the edema status of the corresponding tissues at the key structures, including edema volume, volume change rate, edema depth, and depth velocity change. The edema depth velocity change is the speed of movement out of the tissue, and movement into the tissue is marked as a negative value. The abnormality of the corresponding parameter is obtained by dividing the edema volume, volume change rate, edema depth, and depth velocity change by the safety value of the corresponding parameter. The abnormality of each parameter of the edema status is obtained by weighted summation. The third step is to obtain the planned instrument trajectory and the actual instrument trajectory during the automatic operation of the instrument. The average distance between the actual instrument trajectory and the planned instrument trajectory at each point is obtained. The average distance between the actual instrument trajectory and the planned instrument trajectory at each point is divided by the allowable error of the instrument operation to obtain the instrument operation abnormality. The fourth step involves obtaining the abnormal edema, abnormal cutting speed, and abnormal instrument operation of the corresponding tissues at the key structures, multiplying them by their respective influence coefficients, and then summing them to obtain the operational influence abnormalities.

5. The intraoperative ultrasound examination system for glioblastoma according to claim 1, characterized in that, The process of assessing and issuing warnings regarding surgical pathway safety based on operational impact analysis results includes the following specific aspects: The first step is to obtain the real-time distance of the residual tumor to the key structures in the corresponding direction of movement, as well as the safe distance set before the operation; The second step involves summing the abnormal impact of the operation with the value 1 and then multiplying it by the pre-set safe distance to obtain the actual safe distance. This actual safe distance is then compared with the real-time distance of the critical structure in the corresponding direction of movement. If the actual safe distance is greater than or equal to the real-time distance of the critical structure in the corresponding direction of movement, the original surgical path is considered unsafe, and a warning for the original surgical path is issued. If the actual safe distance is less than the real-time distance of the critical structure in the corresponding direction of movement, the original surgical path is considered safe, and no warning for the original surgical path is issued.

6. The intraoperative ultrasound examination system for glioblastoma according to claim 1, characterized in that, The selection of a surgical path based on the safety assessment results includes the following specific aspects: The process involves obtaining information on potential surgical paths and selecting a path where all points on the path are at least equal to the actual safe distance from the critical structure as a secondary alternative. If no such path exists, the process involves obtaining the number of neurons and blood flow at the intersection of the candidate and selected surgical paths within the actual safe distance range with the critical structure region. The standardized number of neurons and blood flow are obtained by dividing these values ​​by the corresponding safe values. The weighted sum of these values ​​yields the path's risk value, and the surgical path with the lowest risk value at the intersection is selected as the secondary alternative.

7. The intraoperative ultrasound examination system for glioblastoma according to claim 1, characterized in that, The display of the selected surgical path includes the following specific content: During the surgery, secondary alternative paths are obtained in real time based on changes in the surgical situation. These alternative paths are then imported into the surgical equipment and displayed on the real-time surgical screen. The surgical equipment operates according to the real-time alternative paths.

8. A method for intraoperative ultrasound examination of glioblastoma, implemented based on the intraoperative ultrasound examination system for glioblastoma according to any one of claims 1-7, characterized in that, The specific steps include the following: Obtain information about the brain near the tumor location and annotate key structures; Continuously track the spatial position and posture of the patient's head and surgical instruments, and calibrate the marked positions of key structures; The operational impact was analyzed based on the tumor's location and morphological changes, the response of the surrounding environment, and the vibration of the surgical instruments during the cutting process. Based on the results of the operational impact analysis, assess the safety of the surgical pathway and issue early warnings. The surgical approach is selected based on the safety assessment results. Used to display the selected surgical path.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the intraoperative ultrasound examination method for glioblastoma as described in claim 8 by calling the computer program stored in the memory.

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