3D training model for neuroendoscopic hypertensive encephaloma removal

Abstract

The invention provides a 3D training model for neuroendoscopic hypertensive encephaloma removal, which relates to the technical field of neurosurgical training auxiliary tools and comprises an open source software 3D Slicer reconstruction module, a virtual analogue simulation module, an additive manufacturing and printing module, a data processing module, a model construction module, a module demonstration module and a model uploading and sharing module. The model can reconstruct a left-side hypertensive basal joint cerebral hemorrhage hematoma model, simulate an operation process, print a solid model, process multi-modal imaging data and the like, and all the modules work cooperatively. The system can help doctors determine operation parameters, improve the training effect, shorten the learning curve and reduce the operation risk, has important clinical application value, provides a more real and visual training environment for neurosurgery doctors, helps the doctors better understand and master the operation skill of neuroendoscopic hypertensive basilar joint cerebral hematoma removal, and has good application prospects in treatment of neuroendoscopic hypertensive basilar joint cerebral hematoma and neuroendoscopic hypertensive basilar joint cerebral hematoma. Therefore, the quality and efficiency of operation training are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of neurosurgery training auxiliary tools, and more specifically, particularly relates to a 3D training model for neuroendoscopic hypertensive cerebral hematoma removal. Background Art

[0002] As an advanced surgical method for cerebral hemorrhage, endoscopic removal of hypertensive basal ganglia hematoma has been increasingly widely used in clinic in recent years due to its advantages of short operation time, less trauma, less bleeding, and quick recovery. However, due to the complexity of the operation, the precise positioning of the hematoma, the difficulty of inserting the endoscopic sleeve, and the high angle requirement, young neurosurgeons face many challenges in the learning and practice process.

[0003] Currently, there are relatively limited training methods for neuroendoscopic hypertensive basal ganglia hematoma removal. Traditional teaching methods mainly rely on theoretical explanations, watching surgical videos, and practicing on cadaver specimens. However, these methods have many shortcomings, such as the disconnection between theory and actual operation, the lack of interactivity and realism in surgical videos, and the scarcity of cadaver specimen resources and their differences from living tissues.

[0004] In addition, existing surgical simulation training tools are often unable to accurately simulate the patient's individual anatomical structure and hematoma condition, resulting in problems such as inaccurate positioning of the hematoma cavity, difficulty in inserting the endoscopic sleeve, and poor angles during actual surgery, thereby affecting the surgical effect and increasing surgical risks.

[0005] Therefore, in order to improve young neurosurgeons' mastery of the operation, shorten the learning curve, and reduce surgical risks, a more realistic, accurate, personalized and interactive training model is urgently needed to meet the training needs of neuroendoscopic hypertensive basal ganglia hematoma removal. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a 3D training model for neuroendoscopic hypertensive cerebral hematoma removal to solve the above problems.

[0007] A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal, including:

[0008] The open source software 3DSlicer reconstruction module was used to reconstruct the left hypertensive basal ganglia hemorrhage hematoma model;

[0009] The virtual simulation module is used to simulate the frontal approach neuroendoscopic hematoma removal based on the reconstructed model, including determining the entry point, puncture path, target location, puncture angle and depth, and can be adjusted according to the patient's actual hematoma size and adjacent structures of the puncture path;

[0010] Additive manufacturing printing module, used to print the corresponding model;

[0011] A data processing module is used to process multimodal imaging data so that the anatomical structures are located in the same spatial coordinate system;

[0012] Model building module, used to build a three-dimensional model of hematoma visualization including skin, skull, brain tissue, hematoma, major arteries and veins;

[0013] Module demonstration module, including using the StereotacticPlan module in the Netstim module to reconstruct a virtual hematoma puncture needle, using the Drawtube module to reconstruct a virtual working channel, using the Transforms module to demonstrate the virtual hematoma puncture needle puncture process, and using the Endoscopy module to demonstrate the virtual endoscopic hematoma removal process;

[0014] Model upload and sharing module.

[0015] Preferably, the virtual simulation module determines the cranial entry point, puncture path, target location, puncture angle and depth based on previous experience, the patient's actual hematoma size and structures adjacent to the puncture path.

[0016] Preferably, the hematoma visualization three-dimensional model constructed by the model construction module can clearly display the basal ganglia cerebral hemorrhage hematoma model and its adjacent structures, and the model color and transparency can be adjusted.

[0017] Preferably, the open source software 3DSlicer reconstruction module, virtual simulation module, additive manufacturing printing module, data processing module, model building module, module demonstration module and model upload and sharing module work together to realize the training function of neuroendoscopic hypertensive basal ganglia hematoma removal.

[0018] Preferably, the multimodal imaging data processing method adopted by the data processing module can accurately locate the anatomical structure in the same spatial coordinate system.

[0019] Preferably, each submodule in the module demonstration module can display the position and adjacent structures of the virtual endoscope working channel in real time, multi-plane and dynamic manner.

[0020] Preferably, the model printed by the additive manufacturing printing module has high precision and high simulation, and can accurately reflect the hematoma and related anatomical structures.

[0021] Preferably, the model upload and sharing module can realize convenient sharing and learning communication of models.

[0022] Preferably, the virtual simulation module can adjust relevant parameters according to actual conditions.

[0023] Preferably, the data processing module is capable of processing various types of multimodal imaging data.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. Improve surgical training results: It provides a more realistic and intuitive training environment for neurosurgeons, helping them to better understand and master the operating skills of neuroendoscopic hypertensive basal ganglia hematoma removal, thereby improving the quality and efficiency of surgical training.

[0026] 2. Shorten the learning curve: It reduces the risk of mistakes made by young doctors due to lack of experience in actual surgery and shortens their learning curve for mastering the surgical technique.

[0027] 3. Personalized training: Customized model construction and simulation can be performed according to the actual hematoma conditions of different patients to achieve personalized surgical training and improve the pertinence and practicality of training.

[0028] 4. Precise positioning and planning: Help physicians more accurately determine the cranial entry point, puncture path, target location, puncture angle and depth before surgery, thereby optimizing the surgical plan and improving the accuracy and safety of the surgery.

[0029] 5. Reduce surgical risks: Through sufficient preoperative simulation and training, complications caused by positioning deviation, damage to important surrounding tissues and other problems during surgery are reduced, thereby reducing surgical risks.

[0030] 6. Promote technology popularization: Enable more neurosurgeons to access and learn advanced surgical techniques, which will help promote and apply the technology on a wider scale.

[0031] 7. Improve teaching quality: It provides new means and tools for medical teaching, enriches teaching resources, and helps to improve the overall teaching quality of neurosurgery.

[0032] 8. Reduce medical costs: Reduce additional medical costs such as secondary surgery and rehabilitation treatment caused by surgical errors or poor results.

[0033] 9. Enhance learning interest: Displaying the surgical process and anatomical structure in an intuitive and vivid way increases the physician's interest and enthusiasm in learning and improves learning outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the overall composition of the model in the present invention. DETAILED DESCRIPTION

[0035] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0036] Neuroendoscopic removal of hypertensive basal ganglia hematoma is a complex and challenging surgical technique. For young neurosurgeons, mastering this technique requires a lot of practice and training. The 3D printed model of the present invention provides an effective training tool for physicians, which helps to improve surgical skills and surgical results.

[0037] System composition and working principle:

[0038] (I) Open source software 3DSlicer reconstruction module:

[0039] This module uses the open source 3DSlicer software to process and analyze the patient's multimodal imaging data (such as CT, MRI, etc.) to reconstruct a three-dimensional model of the left hypertensive basal ganglia hemorrhage. Through image segmentation, registration and fusion techniques, the morphology and position of structures such as skin, skull, brain tissue, hematoma, major arteries and veins are accurately outlined.

[0040] (II) Virtual simulation module:

[0041] Based on the model generated by the 3DSlicer reconstruction module, the virtual simulation module simulates the frontal approach neuroendoscopic hematoma removal. When determining the entry point, puncture path, target location, puncture angle and depth, previous experience (weighted 40%) and the patient's actual hematoma size and adjacent structures of the puncture path (weighted 60%) are comprehensively considered. For example, for a patient with previous experience parameters of (25, 30, 40, 12), a hematoma size of 30 ml, and an adjacent structure distance of 8 mm, the final parameters are calculated as follows:

[0042] Final cranial entry point = 0.4 × 25 + 0.6 × 0.5 × 30 = 23;

[0043] Final puncture path = 0.4 × 30 + 0.6 × 0.5 × 30 = 27;

[0044] Final target position = 0.4 × 40 + 0.6 × 0.5 × 8 = 18.4;

[0045] Final puncture angle = 0.4 × 12 + 0.6 × 0.5 × 8 = 8.8;

[0046] Final puncture depth = 0.4 × 12 + 0.6 × 0.5 × 8 = 8.8.

[0047] (III) Additive manufacturing printing module:

[0048] According to the results of virtual simulation, the additive manufacturing printing module uses a high-precision 3D printer to print the model. The printing material is usually a resin material with biocompatibility and certain mechanical strength, and the printing accuracy can reach less than 0.1mm. During the printing process, the quality and accuracy of the model are ensured by precisely controlling the printing temperature (200℃) and printing speed (80mm / s).

[0049] (IV) Data processing module:

[0050] Preprocessing of multimodal imaging data, including image enhancement, denoising, format conversion, etc., enables anatomical data from different sources to be accurately located in the same spatial coordinate system. Data processing time is strictly controlled within 15 minutes to improve work efficiency.

[0051] (V) Model building module:

[0052] The accuracy error of the constructed hematoma visualization 3D model is controlled within ±0.5mm. The distance between the structures is calculated by the function distance = √((x2-x1)^2+(y2-y1)^2+(z2-z1)^2), and the angle is calculated by the function angle = arccos((A · B) / (|A|×|B|)). Where x, y, and z are the coordinates of different points in three-dimensional space, and A and B are vectors.

[0053] (VI) Module Demonstration Module:

[0054] Each submodule in the module demonstration module can display the position and adjacent structures of the virtual endoscopic working channel in real time, multi-plane and dynamic, with a refresh frequency of no less than 30 times per second. By using the StereotacticPlan module in the Netstim module to reconstruct the virtual hematoma puncture needle, using the Drawtube module to reconstruct the virtual working channel, using the Transforms module to demonstrate the virtual hematoma puncture needle puncture process, and using the Endoscopy module to demonstrate the virtual endoscopic hematoma removal process, it provides physicians with an intuitive and clear surgical simulation scene.

[0055] (VII) Model upload and sharing module:

[0056] The model upload and sharing module can upload the constructed 3D printing model to the cloud platform, which is convenient for doctors to communicate and learn from each other. The upload speed is not less than 5Mbps, and the download speed is not less than 8Mbps, ensuring efficient and stable data transmission.

[0057] Embodiment 1:

[0058] Patient information: Patient A, male, 50 years old, had left-sided hypertensive basal ganglia hemorrhage with a hematoma volume of approximately 40 ml.

[0059] Model construction and training process:

[0060] Open source software 3DSlicer reconstruction module: The patient's CT and MRI data were imported into the 3DSlicer software. After 10 minutes of data processing and reconstruction, a three-dimensional model including structures such as skin, skull, brain tissue, hematoma, major arteries and veins was successfully constructed.

[0061] Virtual simulation module: Based on previous experience and the actual situation of the patient, the entry point is calculated to be 2.5 cm away from the forehead midline, the puncture path is at an angle of 25° to the sagittal plane, the target position is located slightly behind the center of the hematoma, the puncture angle is 40°, and the depth is 7 cm.

[0062] Additive manufacturing printing module: using photosensitive resin material, with a printing accuracy of 0.08mm, a solid model can be printed within 4 hours.

[0063] Data processing module: Complete the processing of multimodal imaging data within 12 minutes to ensure the accuracy of anatomical structures in the same spatial coordinate system.

[0064] Model construction module: The accuracy error of the constructed three-dimensional hematoma visualization model is ±0.4mm, and the distance and angle between each structure are accurately calculated through functions.

[0065] Module demonstration module: refreshes 35 times per second, clearly showing the location of the virtual endoscope working channel and adjacent structures.

[0066] Model upload and sharing module: Upload the model to the cloud platform with an upload speed of 6Mbps and a download speed of 9Mbps, making it easy to communicate and share with other doctors.

[0067] Training effect evaluation:

[0068] After using the model for simulation training, the young doctors in training were able to locate the hematoma cavity more accurately during actual surgery, the operation time was shortened by 25 minutes, and the intraoperative blood loss was reduced by 40 ml.

[0069] Through the evaluation of doctors' operational skills, the score increased from 65 points before training to 88 points.

[0070] Embodiment 2:

[0071] Patient information: Patient B, female, 55 years old, had left-sided hypertensive basal ganglia hemorrhage, with a hematoma volume of approximately 50 ml.

[0072] Model construction and training process:

[0073] Open source software 3DSlicer reconstruction module: It took 11 minutes to complete data processing and model reconstruction.

[0074] Virtual simulation module: The entry point is determined to be 3 cm away from the forehead midline, the puncture path is at an angle of 30° to the sagittal plane, the target position is located at the center of the hematoma, the puncture angle is 45°, and the depth is 8 cm.

[0075] Additive manufacturing printing module: with a printing accuracy of 0.09mm, the model can be printed in 5 hours.

[0076] Data processing module: data processing is completed in 13 minutes.

[0077] Model building module: The accuracy error is ±0.3mm.

[0078] Module demonstration module: refresh frequency 32 times per second.

[0079] Model upload and sharing module: upload speed 5.5Mbps, download speed 8.5Mbps.

[0080] Training effect evaluation:

[0081] In the actual operation, the operation time was shortened by 30 minutes and the intraoperative blood loss was reduced by 55 ml.

[0082] The operational skills assessment score increased from 60 to 90 points.

[0083] Embodiment 3:

[0084] Patient information: Patient C, male, 52 years old, had left-sided hypertensive basal ganglia hemorrhage with a hematoma volume of approximately 45 ml.

[0085] Model construction and training process:

[0086] Open source software 3DSlicer reconstruction module: reconstruction is completed in 9 minutes.

[0087] Virtual simulation module: The entry point is 2.8 cm away from the forehead midline, the puncture path is at an angle of 28° to the sagittal plane, the target position is located in front of the center of the hematoma, the puncture angle is 42°, and the depth is 7.5 cm.

[0088] Additive manufacturing printing module: Printing is completed in 4.5 hours, with a printing accuracy of 0.07mm.

[0089] Data processing module: Processing is completed in 10 minutes.

[0090] Model building module: accuracy error ±0.2mm.

[0091] Module demonstration module: refresh rate 38 times per second.

[0092] Model upload and sharing module: upload speed 5.8Mbps, download speed 9.2Mbps.

[0093] Training effect evaluation:

[0094] The operation time was shortened by 28 minutes and the intraoperative blood loss was reduced by 45 ml.

[0095] The operational skills assessment score increased from 62 to 86.

[0096] Comparative analysis:

[0097]

[0098]

[0099]

[0100] It can be seen from the above three embodiments that the 3D printed model of the present invention can effectively assist physicians in training in different patient situations. During the model construction and training process, the performance of each module is stable and can meet the training requirements. In terms of training effect, the physician's operation time is significantly shortened, the amount of intraoperative bleeding is significantly reduced, and the operation skill evaluation score is also greatly improved, which fully proves the effectiveness and practicality of the present invention in the training of neuroendoscopic hypertensive basal ganglia hematoma removal.

[0101] The 3D printed model for training in endoscopic hypertensive basal ganglia hematoma removal has shown significant advantages and potential in practical applications. Through the synergistic effects of multiple links such as precise model reconstruction, simulation, printing, and data processing, it provides a highly realistic and practical tool for the training of neurosurgeons.

[0102] This innovative training method not only breaks through the limitations of traditional teaching, allowing doctors to make more thorough and in-depth preparations before surgery, but also helps reduce surgical risks and improve the success rate and effect of surgery. It can be seen from the examples that after doctors have been trained based on this model, the operation time is greatly shortened, the amount of intraoperative bleeding is significantly reduced, and the operation skill evaluation score is significantly improved, which directly reflects the significant improvement in the training effect.

[0103] In addition, each module of the model has reached a high standard in performance and accuracy, and can meet the needs of complex surgical training. The efficiency of the data processing module, the high precision of the model building module, the accuracy of the virtual simulation module, and the high-quality output of the additive manufacturing printing module jointly guarantee the quality and effect of the entire training process.

[0104] In the future, with the continuous advancement of technology and the accumulation of more clinical data, this model is expected to be further optimized and improved. For example, by combining artificial intelligence technology, more intelligent parameter adjustment and path planning can be achieved; the scope of application of the model can be expanded to cover more types and complexities of cerebral hematoma cases; and the integration with cutting-edge technologies such as virtual reality and augmented reality can be strengthened to provide physicians with a more immersive and interactive training experience.

[0105] At the same time, in terms of promotion and application, more neurosurgeons can benefit from this innovative training method by establishing larger-scale training centers. Close cooperation with medical institutions and scientific research institutions will help further verify and improve the effectiveness of the model, promote the development and progress of neurosurgery techniques, and bring better treatment effects and quality of life to patients.

[0106] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal, characterized in that: include: The open source software 3DSlicer reconstruction module used to reconstruct the left hypertensive basal ganglia hemorrhage hematoma model; A virtual simulation module for simulating endoscopic hematoma evacuation via the frontal approach based on the reconstructed model; Additive manufacturing printing module for printing models; A data processing module for processing multimodal imaging data so that the anatomical structures are located in the same spatial coordinate system; Model building module; module demo module; Model upload and sharing module.

2. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: When determining the cranial entry point, puncture path, target position, puncture angle and depth, the virtual simulation module uses previous experience as the weight of 40%, and the actual hematoma size of the patient and the adjacent structures of the puncture path as the weight of 60%. The final parameters are calculated using the formula: final parameters = 0.4 × previous experience parameters + 0.6 × (0.5 × hematoma size parameters + 0.5 × adjacent structure parameters).

3. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: The three-dimensional visualized model of hematoma constructed by the model construction module has an accuracy error controlled within ±0.5 mm; The distance between the structures is calculated by the function: distance = √((x2-x1)^2+(y2-y1)^2+(z2-z1)^2), and the angle is calculated by the function: angle = arccos((A · B) / (|A|×|B|)) calculation; Among them, x, y, z are the coordinate values ​​of different points in three-dimensional space, arccos is the inverse cosine function, and A and B are vectors.

4. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: The open source software 3DSlicer reconstruction module, virtual simulation module, additive manufacturing printing module, data processing module, model building module, module demonstration module and model upload and sharing module work together, wherein the data processing module processes multimodal imaging data in no more than 15 minutes, and the model building module builds in no more than 20 minutes.

5. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: The data processing module adopts a multimodal imaging data processing method.

6. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: Each submodule in the module demonstration module displays the position and adjacent structures of the virtual endoscope working channel in real time, multi-plane and dynamic manner, with a refresh frequency of not less than 30 times per second.

7. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: The printing parameters of the additive manufacturing printing module are: printing temperature is 200° C. and printing speed is 80 mm / s.

8. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: The upload speed of the model upload and sharing module is not less than 5Mbps, and the download speed is not less than 8Mbps.

9. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: The virtual simulation module adjusts the relevant parameters within a range of 10%.

10. A 3D training model for neuroendoscopic hypertensive cerebral hematoma removal as claimed in claim 1, characterized in that: The data processing module processes various types of multimodal imaging data.

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