Anesthesia puncture visual navigation system combining augmented reality and three-dimensional reconstruction

Abstract

The invention relates to an anesthesia puncture visual navigation system combining augmented reality and three-dimensional reconstruction, and belongs to the technical field of anesthesia puncture visual navigation. The anesthesia puncture visual navigation system comprises a physiological movement monitoring unit used for simultaneously collecting respiratory movement cycle data and soft tissue elastic deformation information; a comprehensive motion state data packet is generated by calculating the correlation coefficient of the breathing amplitude and the tissue elasticity modulus change rate, the space deviation caused by breathing motion and soft tissue deformation of a patient to puncture navigation can be effectively compensated, the puncture precision is remarkably improved compared with a traditional static navigation system under the normal breathing state of the patient, and the puncture accuracy is improved. The puncture failure rate and the complication risk are effectively reduced, the problem that precision is further deteriorated due to the fact that correction parameters are still used blindly when registration quality is deteriorated in a traditional system is solved, dependence on personal experience of doctors is reduced, and the problem that the system is completely invalid possibly caused by a single correction mode is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of anesthetic puncture visualization navigation, in particular to an anesthetic puncture visualization navigation system combining augmented reality and three-dimensional reconstruction. BACKGROUND

[0002] Traditional anesthetic puncture navigation technology mainly relies on static image data and fixed anatomical reference points for path planning and navigation. However, in actual clinical application, the following technical defects exist: During the anesthetic puncture process, the patient will inevitably produce respiratory motion, heartbeat and slight body position changes. These physiological motions will cause real-time changes in the spatial position of the target anatomical structure. The traditional navigation system cannot effectively identify and compensate for these dynamic changes, resulting in a significant decrease in puncture accuracy and an increase in the risk of puncture failure and complications.

[0003] The prior art lacks effective processing capability for soft tissue elastic deformation. When the puncture needle contacts and compresses the soft tissue, the tissue will undergo elastic deformation to varying degrees, changing the original anatomical structure relationship. The traditional system cannot real-time perceive and compensate for this deformation, causing deviations in the navigation information and the actual anatomical position. The current registration technology lacks a dynamic quality evaluation mechanism. In the environment of patient motion interference, the quality of image registration will change with time and motion amplitude. The existing system cannot real-time evaluate the reliability of registration quality, nor can it dynamically adjust the correction strategy according to the quality changes, resulting in maintaining the original correction intensity when the registration quality decreases, further reducing the navigation accuracy.

[0004] The above information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide an anesthetic puncture visualization navigation system combining augmented reality and three-dimensional reconstruction to solve the problems raised in the above background.

[0006] The technical solution of the present application is: comprising: a physiological motion monitoring unit for simultaneously collecting respiratory motion cycle data and soft tissue elastic deformation information, generating a comprehensive motion state data package by calculating the correlation coefficient of respiratory amplitude and tissue elastic modulus change rate; A three-dimensional reconstruction processing unit is used to construct a three-dimensional anatomical model of the patient based on multi-modal medical image data, receive the comprehensive motion state data package, and dynamically adjust the three-dimensional anatomical model according to the motion parameters to generate a dynamic anatomical reference model; A spatial position correction unit is configured to receive a comprehensive motion state data packet and a dynamic anatomical reference model, predict the position of the target anatomical structure based on the respiratory motion cycle data, evaluate the puncture needle position offset based on the soft tissue elastic deformation information, and perform weighted fusion of the predicted position and offset to generate correction parameters; The multimodal registration quality assessment unit is used to receive real-time medical imaging data, dynamic anatomical reference models, and comprehensive motion state data packets, calculate spatial position entropy, texture feature entropy, and motion consistency entropy, and perform weighted fusion of the three types of entropy values ​​to generate a comprehensive quality assessment index; A dynamic compensation adjustment unit is used to receive the comprehensive quality assessment index, determine the correction intensity level according to the index value, and generate a correction intensity control parameter to output to the spatial position correction unit; The augmented reality navigation unit is used to receive the correction parameters adjusted by the correction intensity control parameters, superimpose the navigation information on the real-time medical image, and perform hierarchical visual coding according to the correction credibility.

[0007] Preferably, the comprehensive motion state data packet includes a timestamp, a three-dimensional motion amplitude vector, a tissue elasticity change, and a motion deformation correlation coefficient.

[0008] Preferably, the spatial position correction unit receives the correction strength control parameter from the dynamic compensation adjustment unit to adjust the application ratio of the correction parameter, and generates a final correction parameter to output to the augmented reality navigation unit.

[0009] Preferably, the dynamic compensation adjustment unit receives navigation effect evaluation data from the augmented reality navigation unit for optimizing the mapping relationship between the quality index and the correction intensity.

[0010] Preferably, the physiological motion monitoring unit includes a strain-type respiratory sensor and a contact ultrasonic transducer. The strain-type respiratory sensor is deployed on the patient's chest and abdomen to monitor respiratory motion parameters, and the contact ultrasonic transducer is used to detect changes in the elastic modulus of the soft tissue in the puncture area.

[0011] Preferably, in the weighted fusion, the weight of the spatial position entropy is 40%, the weight of the texture feature entropy is 35%, and the weight of the motion consistency entropy is 25%.

[0012] Preferably, the correction intensity level includes a high intensity level, a medium intensity level and a low intensity level. When the comprehensive quality evaluation index is higher than 0.8, it is determined to be a high intensity level; when the index is between 0.5 and 0.8, it is determined to be a medium intensity level; when the index is lower than 0.5, it is determined to be a low intensity level.

[0013] Preferably, the high intensity level corresponds to a 100% application ratio, the medium intensity level corresponds to a 60% application ratio, and the low intensity level corresponds to a 30% application ratio.

[0014] Preferably, the hierarchical visual coding includes color coding and transparency coding, high calibration reliability corresponds to green solid line display, medium calibration reliability corresponds to yellow dotted line display, and low calibration reliability corresponds to red semi-transparent display.

[0015] The present application provides a anesthesia puncture visualization navigation system combined with augmented reality and three-dimensional reconstruction by improvement, compared with the prior art, has the following improvements and advantages: By establishing a physiological motion adaptive dynamic compensation mechanism, the present application can effectively compensate for the spatial deviation caused by patient respiratory motion and soft tissue deformation to puncture navigation, and in the normal breathing state of the patient, the puncture accuracy is significantly improved compared with the traditional static navigation system, effectively reducing the puncture failure rate and complication risk. Innovatively introduce a registration quality evaluation mechanism based on multi-modal information entropy, realize real-time quantitative monitoring of the reliability of the navigation system, can timely issue a warning when the registration quality decreases and automatically adjust the correction strategy, avoid the further deterioration of the precision caused by the blind application of correction parameters in the traditional system when the registration quality deteriorates; A complete adaptive learning loop from physiological motion perception to quality evaluation to dynamic correction is established, which can automatically optimize the correction strategy according to the physiological characteristics and motion patterns of different patients, improve the navigation accuracy for specific patient groups, and significantly reduce the dependence on the personal experience of the doctor; A hierarchical visualization navigation interface based on confidence is provided through augmented reality technology, and the doctor can intuitively judge the reliability of the navigation information and make corresponding decisions, which greatly improves the safety and efficiency of the puncture operation and reduces the average puncture time; The multiple-coupled adaptive mechanism ensures the stability of the system when facing various sudden motion disturbances, even in extreme cases such as severe coughing or sudden change of body position, the system can still maintain basic navigation function through dynamic adjustment of correction strength, avoiding the complete failure of the system caused by a single correction mode. BRIEF DESCRIPTION OF DRAWINGS

[0016] The present application will be further explained in conjunction with the accompanying drawings and examples: Figure 1 is a flow chart of the system of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further explained in detail in conjunction with specific examples. EMBODIMENT

[0018] Please refer to Figure 1The application provides a technical scheme of anesthetic puncture visualization navigation system combining augmented reality and three-dimensional reconstruction, comprising: a physiological motion monitoring unit for simultaneously collecting respiratory motion cycle data and soft tissue elastic deformation information, and generating a comprehensive motion state data package by calculating the correlation coefficient of respiratory amplitude and tissue elastic modulus change rate; A three-dimensional reconstruction processing unit is used for constructing a three-dimensional anatomical model of a patient based on multi-modal medical image data, receiving the comprehensive motion state data package, and generating a dynamic anatomical reference model by dynamically adjusting the three-dimensional anatomical model according to the motion parameters; A spatial position correction unit is used for receiving the comprehensive motion state data package and the dynamic anatomical reference model, predicting the position of a target anatomical structure according to the respiratory motion cycle data, evaluating the position offset of a puncture needle according to the soft tissue elastic deformation information, and generating a correction parameter by weighted fusion of the predicted position and the offset; A multi-modal registration quality evaluation unit is used for receiving real-time medical image data, a dynamic anatomical reference model and a comprehensive motion state data package, calculating spatial position entropy, texture feature entropy and motion consistency entropy, and generating a comprehensive quality evaluation index by weighted fusion of the three types of entropy values; A dynamic compensation adjustment unit is used for receiving the comprehensive quality evaluation index, determining the correction intensity level according to the index value, and generating a correction intensity control parameter output to the spatial position correction unit; An augmented reality navigation unit is used for receiving the correction parameter adjusted by the corrected intensity control parameter, superimposing navigation information on real-time medical images, and performing hierarchical visualization coding according to the correction reliability; In the embodiment, the workflow of the above system is described; the core design concept of the application is to construct a physiological motion perception-quality evaluation-dynamic correction triple-coupling adaptive closed-loop mechanism; the essential difference from the prior art is that, instead of simply aggregating various functional modules, the application realizes deep cooperation and intelligent linkage among the units through innovative data flow and control flow design, solves the problems of navigation accuracy decline and reliability quantization caused by patient physiological motion and instrument interaction; The system starts with the physiological motion monitoring unit, which quantifies two core aspects of patient physiological motion in real time and synchronously; this unit collects the period, amplitude, and phase data of respiratory motion through sensors deployed on the patient's chest and abdomen, and simultaneously collects the soft tissue elastic modulus changes caused by the puncture needle pressure or respiratory motion using a probe located in the puncture area; to ensure data quality, the raw sensor signals are subjected to digital filtering to eliminate motion artifacts and noise; the innovative processing logic of this unit is that it does not independently collect two sets of data, but quantifies the coupling strength of respiratory motion to the tissue deformation in the puncture area by calculating the Pearson correlation coefficient of the two within a certain sliding time window; this generates a structured comprehensive motion state data package, which is distributed to the subsequent three processing units; The three-dimensional reconstruction processing unit receives preoperative CT or MRI and other multi-modal medical images, uses a pre-trained convolutional neural network algorithm for semantic segmentation, and constructs a static three-dimensional anatomical model containing key structures such as blood vessels and nerves; the key role of this unit is to give the static model dynamic vitality; it receives the comprehensive motion state data package from the physiological motion monitoring unit and, based on the motion vectors and deformation parameters therein, inputs them as boundary conditions and loads into a pre-set finite element analysis model; by solving the model, the system can dynamically adjust the position and shape of the three-dimensional anatomical model in accordance with the laws of biomechanics in real time; the output is a dynamic anatomical reference model highly synchronized with the patient's current physiological state, which is transmitted to the spatial position correction unit and the multi-modal registration quality assessment unit; At the same time, the multi-modal registration quality assessment unit performs its core quality monitoring function; this unit receives three inputs: real-time medical images, the aforementioned dynamic anatomical reference model, and the comprehensive motion state data package; the processing logic is based on the principle of information entropy to assess the reliability of the current registration state from three dimensions: By calculating the Euclidean distance probability distribution of the corresponding anatomical landmark point clusters in the model and real-time images, the spatial position entropy is quantified; the more dispersed the distribution, the higher the entropy value, representing a poorer degree of position alignment; By calculating the normalized mutual information of the corresponding region, the texture feature entropy is quantified; the lower the mutual information, the poorer the texture similarity, and the higher the entropy value; By comparing the difference between the motion field predicted by the model and the motion field observed from the real-time image sequence based on the optical flow method, the motion consistency entropy is quantified; the larger the difference, the higher the entropy value; The three types of entropy values are weighted and fused to generate a single, quantitative comprehensive quality assessment index; this index is the system's real-time and objective self-score of the reliability of its own working state, and is output to the dynamic compensation adjustment unit; The dynamic compensation adjustment unit, as the core control logic module of the system, intelligently determines the intervention strength of the correction operation according to the reliability of the current registration of the system. It receives the comprehensive quality evaluation index, and determines it as high, medium and low three levels according to the preset threshold. The unit generates a correction strength control parameter according to this, and outputs it to the spatial position correction unit; The spatial position correction unit is an execution unit for executing specific correction tasks. It receives the comprehensive motion state data packet and the dynamic anatomical reference model. The unit uses an autoregressive moving average model to predict the position of the target anatomical structure in a very short time in the future according to the historical respiratory cycle data. Based on the theory of continuum mechanics, combined with the elastic deformation information of soft tissue, the possible deviation caused by the puncture needle is evaluated. The two prediction results are weighted and fused to generate a preliminary correction parameter. In the specific configuration of this embodiment, the unit receives the correction strength control parameter from the dynamic compensation adjustment unit, and uses the parameter as a gain factor to modulate the application amplitude of the preliminary correction parameter. This generates a final correction parameter that has been adjusted in strength, and outputs it to the augmented reality navigation unit. The augmented reality navigation unit, as a human-computer interaction interface, receives the final correction parameter and converts it into visual navigation information such as graphical puncture path, target point and safety boundary. It uses graphical rendering technology to superimpose these information on real-time medical images in a graphical way. Its innovation lies in that, according to the correction confidence information contained in the correction parameter, the navigation graphics are hierarchically visualized and encoded. The encoding directly maps the correction confidence index to a preset lookup table containing color, line type and transparency attributes. This design enables doctors to intuitively judge the reliability of navigation information, so as to make safer clinical decisions. Through the close cooperation and information loop of the above units, the system constructs a complete adaptive navigation process from perception, evaluation to decision, execution, and feedback, which significantly improves the accuracy and safety of anesthesia puncture navigation in dynamic interference environment. Embodiment

[0019] The comprehensive motion state data packet contains a time stamp, a three-dimensional motion amplitude vector, a tissue elasticity change amount and a motion deformation correlation coefficient. To further clarify the mechanism, the comprehensive motion state data packet is designed as a structured data set, which aims to comprehensively and quantitatively describe the instantaneous state of physiological motion and reveal its internal correlation, which is a technical prerequisite for achieving high-precision dynamic compensation; the time stamp is used to ensure the accurate synchronization of all data streams in the system in time, which is the basis for subsequent correlation analysis and dynamic compensation; the three-dimensional motion amplitude vector is used to represent the displacement direction and size of the target region in three-dimensional space caused by motion such as respiration, providing direct geometric input for dynamic adjustment of the three-dimensional model; the tissue elasticity change is a quantitative index measured by ultrasonic elastography technology, which is used to represent the change of soft tissue stiffness caused by the compression of the puncture needle or the movement of internal organs, and is a key parameter for evaluating non-rigid deformation; the motion deformation correlation coefficient is a value calculated in the range of -1 to 1, which represents the linear coupling strength between macroscopic respiratory motion and microscopic tissue deformation; by including information in these four dimensions, the data packet enables the system not only to separate and identify the source of motion and deformation, but also to understand the causal relationship between the two, thereby achieving more accurate and robust dynamic compensation. Embodiment

[0020] The spatial position correction unit receives the application ratio of the correction strength control parameter adjusted by the dynamic compensation adjustment unit to the correction parameter, generates a final correction parameter, and outputs it to the augmented reality navigation unit; In a specific configuration of this embodiment, the interaction between the spatial position correction unit and the dynamic compensation adjustment unit constitutes a key adaptive control loop; the spatial position correction unit receives the motion data and the model, and calculates a theoretically optimal full correction parameter; this theoretical value is based on the ideal premise of complete registration accuracy; to cope with the situation of registration quality fluctuation in reality, the dynamic compensation adjustment unit will generate a correction strength control parameter representing the confidence level according to the real-time comprehensive quality evaluation index; this parameter is passed to the spatial position correction unit, which acts as an adjustment gain to directly modulate the application ratio of the correction parameter; the spatial position correction unit will use this parameter to determine the application amplitude of the full correction parameter; for example, when the control parameter indicates high confidence, the application ratio is 1.0; when it indicates medium confidence, the application ratio is reduced to 0.6; in this way, the system dynamically links the correction intensity with the registration reliability, avoiding excessive or incorrect correction when the registration quality is poor, thereby generating a more robust and safe final correction parameter, which is output to the augmented reality navigation unit. Embodiment

[0021] The dynamic compensation adjustment unit receives navigation effect evaluation data from the augmented reality navigation unit to optimize the mapping relationship between the quality index and the correction strength; To achieve the continuous self-optimization of the system, a learning feedback loop from the execution end to the control logic end is established in this embodiment; the augmented reality navigation unit not only provides navigation services but also performs effect evaluation functions; it calculates the root mean square error of navigation by comparing the planned puncture path with the actual puncture needle trajectory after the doctor operates (obtained through the tracker), and generates structured navigation effect evaluation data in combination with the doctor's subjective score; the data is fed back to the dynamic compensation adjustment unit; the unit has a learning module based on the gradient descent algorithm inside; the module constructs a cost function, the goal of which is to minimize the navigation error; by receiving the navigation effect evaluation data, the module can calculate the error gradient under the current quality index-correction intensity mapping relationship, and adjust the parameters of the mapping function accordingly; the learning process can be set to update offline after processing a certain number of cases to ensure that the system can adapt to the characteristics of different patient groups or specific procedures, and realize the individualization and intelligent evolution of the navigation strategy. Embodiment

[0022] The physiological motion monitoring unit includes a strain respiratory sensor and a contact ultrasonic transducer, the strain respiratory sensor is deployed on the chest and abdomen of the patient for monitoring respiratory motion parameters, and the contact ultrasonic transducer is used for detecting the change of the elastic modulus of soft tissue in the puncture area; To achieve effective monitoring of physiological motion, the physiological motion monitoring unit in this embodiment adopts a specific hardware configuration; the strain respiratory sensor is designed as a soft band integrated with a piezoelectric or optical fiber sensing element, which is deployed on the chest and abdomen of the patient; when the patient breathes, the fluctuation of the chest will cause the stretching or contraction of the band, and the sensor will convert this physical deformation into an electrical signal at a sampling frequency of not less than 50 Hz, thereby accurately monitoring the period, amplitude and phase of the respiratory motion and other parameters; at the same time, a contact ultrasonic transducer is integrated on the puncture probe or nearby, which is in close contact with the skin of the puncture area; the transducer uses shear wave elastography technology to detect the change of the elastic modulus of soft tissue under the puncture area by emitting sound waves and analyzing the shear wave propagation speed in the tissue at a frame rate of not less than 20 Hz; the combination of this dual-mode sensor enables the system to synchronously capture motion information from both macro and micro levels, providing a comprehensive and accurate data basis for subsequent dynamic compensation. Embodiment

[0023] The weight of the spatial position entropy in the weighted fusion is 40%, the weight of the texture feature entropy is 35%, and the weight of the motion consistency entropy is 25%; In the multi-modal registration quality assessment unit, in order to generate a comprehensive index that can accurately reflect the overall registration quality, the embodiment adopts a specific weighted fusion strategy for the three types of entropy values; the weight of the spatial position entropy is set to the highest 40%; the logical basis is that the spatial position alignment of the anatomical landmark points is the most direct and important indicator for evaluating the success or failure of registration, and is directly related to the geometric accuracy of navigation; the weight of the texture feature entropy is set to 35%; it reflects the consistency of the real-time image and the reference model in the tissue texture details, and is an important supplement to the evaluation of registration accuracy, which plays a significant role in identifying soft tissues lacking clear boundaries; the weight of the motion consistency entropy is set to 25%; this weight is relatively low because it mainly evaluates the consistency of the dynamic process as a verification and supplement to the static position and texture evaluation; these weight coefficients are not set arbitrarily, but are determined through multivariate regression analysis and principal component analysis of large-scale clinical data, aiming to maximize the correlation between the comprehensive evaluation index and the actual navigation accuracy marked by experts. Embodiments

[0024] The correction intensity level includes a high intensity level, a medium intensity level, and a low intensity level, the high intensity level is determined when the comprehensive quality assessment index is higher than 0.8, the medium intensity level is determined when the index is between 0.5 and 0.8, and the low intensity level is determined when the index is lower than 0.5; The high intensity level corresponds to a 100% application ratio, the medium intensity level corresponds to a 60% application ratio, and the low intensity level corresponds to a 30% application ratio; To convert the quantitative quality assessment into a clear control strategy, the adjustment mechanism of the dynamic compensation adjustment unit is specified in detail; the unit establishes a three-level correction intensity level system, and maps the comprehensive quality assessment index (whose value range is 0 to 1) to these levels; When the comprehensive quality assessment index is higher than 0.8, the system determines that the current registration state is in a high intensity level, i.e., a high reliability state; in this state, the system has high confidence in its registration results, and the dynamic compensation adjustment unit outputs a control parameter corresponding to a 100% application ratio; this allows the spatial position correction unit to fully apply the correction amount it calculates, to compensate for errors caused by motion and deformation to the greatest extent; When the index is between 0.5 and 0.8, the system determines that the registration state is in a medium intensity level; this indicates that the registration is basically reliable, but there is some uncertainty; to strike a balance between improving accuracy and avoiding risks, a more cautious strategy is adopted, reducing the application ratio of the correction parameter to 60%; this moderate compensation can correct deviations to a large extent, while avoiding over-amplification of minor errors in registration; When the index is below 0.5, the system determines that the registration state is at a low intensity level, i.e. a low reliable state; this usually means that there is a problem such as severe motion or a significant drop in image quality; at this time, the risk of blindly making a large correction is high; the system will apply a significantly reduced scale of 30%, and the purpose is no longer to pursue perfect precision compensation, but to prioritize the stability and safety of navigation, and only provide basic and directional guidance; It should be understood that the specific values of the two thresholds of 0.8 and 0.5 are not fixed, but are the best working points determined by receiver operating characteristic curve analysis on a large-scale clinical data set, aiming to balance the sensitivity and specificity of correction, so as to optimize the overall navigation performance. Embodiment

[0025] The hierarchical visualization coding includes color coding and transparency coding, high correction reliability corresponds to green solid line display, medium correction reliability corresponds to yellow dashed line display, and low correction reliability corresponds to red semi-transparent display; In order to intuitively convey the confidence state inside the system to the operating doctor, the augmented reality navigation unit in this embodiment adopts a hierarchical visualization coding mechanism which integrates color coding, line coding and transparency coding; When the received correction parameter has high correction reliability (corresponding to a high intensity level), the navigation path will be rendered as a clear green solid line; green represents safety and passage in general cognition, and a solid line represents certainty and stability; this visual presentation provides the doctor with a clear visual prompt about the high fidelity of the navigation data; When the reliability is medium correction reliability (corresponding to a medium intensity level), the navigation path is displayed as a yellow dashed line; yellow is usually used as a warning color, and a dashed line implies the incompleteness of the path; this combination aims to remind the doctor that the system is providing a correction suggestion, but there is a certain degree of uncertainty, which needs to be carefully referred to; When the reliability is low correction reliability (corresponding to a low intensity level), the navigation information (such as the boundary of the danger area) will be displayed in a red semi-transparent manner; red is a strong warning signal, and the semi-transparent effect visually reduces the intensity of its guidance, implying that its reference value is limited; this visual presentation is a warning to the doctor, prompting that the current reliability of the system is low, and the judgment should mainly rely on the original image and clinical experience; Through this intelligent hierarchical visualization design, the system converts the complex internal quality evaluation results into intuitive visual language that the doctor can see at a glance, greatly improving the efficiency of human-computer interaction and the safety of the puncture operation.

[0026] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. An anesthetic puncture visualization navigation system that combines augmented reality with three-dimensional reconstruction, characterized by: The physiological motion monitoring unit is configured to simultaneously collect respiratory motion cycle data and soft tissue elastic deformation information, and generate a comprehensive motion state data package by calculating a correlation coefficient of respiratory amplitude and a change rate of tissue elastic modulus. The three-dimensional reconstruction processing unit is configured to construct a three-dimensional anatomical model of a patient based on multi-modal medical image data, receive the comprehensive motion state data package, and dynamically adjust the three-dimensional anatomical model according to motion parameters to generate a dynamic anatomical reference model. The spatial position correction unit is configured to receive the comprehensive motion state data package and the dynamic anatomical reference model, predict a target anatomical structure position according to the respiratory motion cycle data, evaluate a needle position offset amount according to the soft tissue elastic deformation information, and generate a correction parameter by weighted fusion of the predicted position and the offset amount. The multi-modal registration quality evaluation unit is configured to receive real-time medical image data, the dynamic anatomical reference model, and the comprehensive motion state data package, calculate a spatial position entropy, a texture feature entropy, and a motion consistency entropy, and generate a comprehensive quality evaluation index by weighted fusion of the three types of entropy values. The dynamic compensation adjustment unit is configured to receive the comprehensive quality evaluation index, determine a correction intensity level according to an index value, and generate a correction intensity control parameter to be output to the spatial position correction unit. The augmented reality navigation unit is configured to receive the correction parameter adjusted by the correction intensity control parameter, superimpose navigation information on real-time medical images, and perform hierarchical visual coding according to correction reliability. The comprehensive motion state data package includes a timestamp, a three-dimensional motion amplitude vector, a tissue elastic change amount, and a motion deformation correlation coefficient.

2. The anesthetic puncture visualization and navigation system incorporating augmented reality and three-dimensional reconstruction of claim 1, wherein, The spatial position correction unit receives a correction intensity control parameter from the dynamic compensation adjustment unit to adjust the application proportion of the correction parameter, and generates a final correction parameter to be output to the augmented reality navigation unit.

3. The anesthetic puncture visualization and navigation system incorporating augmented reality and three-dimensional reconstruction of claim 1, wherein, The dynamic compensation adjustment unit receives navigation effect evaluation data from the augmented reality navigation unit to optimize the mapping relationship between the quality index and the correction intensity.

4. The anesthetic puncture visualization and navigation system incorporating augmented reality and three-dimensional reconstruction of claim 1, wherein, The physiological motion monitoring unit includes a strain respiratory sensor and a contact ultrasonic transducer. The strain respiratory sensor is arranged on the chest and abdomen of the patient to monitor respiratory motion parameters. The contact ultrasonic transducer is used to detect the change of the elastic modulus of soft tissue in the puncture area.

5. The augmented reality and three-dimensional reconstruction combined anesthetic puncture visualization navigation system according to claim 1, characterized in that, In the weighted fusion, the weight of the spatial position entropy is 40%, the weight of the texture feature entropy is 35%, and the weight of the motion consistency entropy is 25%.

6. The anesthetic puncture visualization and navigation system incorporating augmented reality and three-dimensional reconstruction of claim 1, wherein, The correction intensity level includes a high intensity level, a medium intensity level, and a low intensity level. When the comprehensive quality evaluation index is higher than 0.8, it is determined as the high intensity level. When the index is between 0.5 and 0.8, it is determined as the medium intensity level. When the index is lower than 0.5, it is determined as the low intensity level.

7. The anesthetic puncture visualization and navigation system incorporating augmented reality and three-dimensional reconstruction of claim 1, wherein, The high intensity level corresponds to a 100% application proportion, the medium intensity level corresponds to a 60% application proportion, and the low intensity level corresponds to a 30% application proportion.

8. The anesthetic puncture visualization and navigation system incorporating augmented reality and three-dimensional reconstruction of claim 7, wherein, The hierarchical visual coding includes color coding and transparency coding. High correction reliability corresponds to green solid line display, medium correction reliability corresponds to yellow dashed line display, and low correction reliability corresponds to red semi-transparent display.

9. The anesthetic puncture visualization and navigation system incorporating augmented reality and three-dimensional reconstruction of claim 1, wherein, ​

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