Temporal lobe epilepsy resection operation curative effect evaluation system based on brain image fusion features

Abstract

The invention discloses a temporal lobe epilepsy resection operation curative effect evaluation system based on brain image fusion features. According to the system, pre-operation and post-operation multi-mode brain image data including T1 weighted images, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) are collected and preprocessed, and a post-operation recovery model is generated through a recurrent neural network (RNN), a long-short-term memory network (LSTM) and other multi-time-sequence analysis methods. And the personalized curative effect evaluation module is combined with clinical characteristic data of the patient. And the report generation and visualization module generates an easy-to-understand report from the evaluation data through a visualization technology for medical teams to check and make decisions. Through multi-modal data fusion and an advanced algorithm model, the accuracy and reliability of curative effect evaluation are improved, the brain structure and function changes of the patient are comprehensively reflected, the clinical application prospect is wide, and the postoperative life quality of the patient is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing and analysis, and in particular to a temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features. Background Art

[0002] Temporal lobe epilepsy is a common type of drug-resistant epilepsy involving key brain structures such as the amygdala of the human brain. Seizures are usually controlled by surgical resection of epileptic foci. As a serious brain disease, epilepsy has an important impact on the quality of life of patients due to its diagnosis and efficacy evaluation. At present, the evaluation of the efficacy of temporal lobe epilepsy resection surgery mainly relies on preoperative and postoperative clinical symptom observation and single-modality brain imaging data analysis, such as using only T1-weighted images or functional magnetic resonance imaging (fMRI) data. However, single-modality imaging analysis often fails to fully reflect the changes in the patient's brain structure and function, resulting in insufficient accuracy and reliability of efficacy evaluation. Although the existing computer-aided diagnosis system can provide certain diagnostic support, it lacks the ability to integrate and analyze multimodal data. In addition, medical data mining technology has performed well in analyzing other patients' cases, but its application in personalized efficacy evaluation is still insufficient. Existing evaluation methods also lack the ability of dynamic adjustment and multi-stage comprehensive analysis, making it difficult to cope with complex brain disease states.

[0003] Therefore, there is an urgent need for a system that can integrate multimodal brain imaging data and use advanced algorithm models for dynamic and comprehensive evaluation, so as to improve the evaluation level of the efficacy of temporal lobe epilepsy resection surgery and meet the needs of medical expert systems and medical data mining in the diagnosis and evaluation of brain diseases. Summary of the invention

[0004] The purpose of the present invention is to provide a temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features, which has the advantages of multimodal data fusion, dynamic feedback loop and staged comprehensive evaluation, and solves the problems of insufficient accuracy of efficacy evaluation and incomplete data analysis in the prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features, the system comprising the following modules: a) a multimodal image data acquisition and preprocessing module, responsible for acquiring and preprocessing preoperative and postoperative T1-weighted images, diffusion tensor imaging and functional magnetic resonance imaging data, the preprocessing steps including denoising, standardization, head motion correction and spatial smoothing; b) a dynamic image fusion and analysis module, which receives the image data preprocessed by the multimodal image data acquisition and preprocessing module, generates a postoperative recovery model using a multi-time series analysis method, and transmits the postoperative recovery model to the personalized efficacy evaluation module; c) A personalized efficacy evaluation module combines the postoperative recovery model and patient characteristic data generated by the dynamic image fusion and analysis module to analyze and generate a personalized postoperative recovery report, which is then transmitted to the abnormality detection and intelligent early warning module; d) Abnormal detection and intelligent early warning module, which monitors abnormal conditions in the postoperative recovery report generated by the personalized efficacy evaluation module in real time, implements error compensation, issues early warning notifications, and transmits the detection results to the report generation and visualization module; e) Data storage and management module, responsible for storing and managing multimodal imaging data, analysis results and patient information; f) Report generation and visualization module, which receives data transmitted by the personalized efficacy evaluation module and the anomaly detection and intelligent early warning module, and generates a visualization report showing the postoperative recovery trend and personalized indicators.

[0006] Preferably, the multimodal image data acquisition and preprocessing module further comprises the following steps: a) Convert the original image data of preoperative and postoperative T1-weighted images, diffusion tensor imaging, and functional magnetic resonance imaging into a standard format; b) De-noise the converted image data using wavelet transform or Gaussian filtering; c) Standardize the de-noised image data and calculate the mean of the image data and standard deviation , and apply the normalization formula: ,in is the mean of the image data, is the standard deviation of the image data, is the standardized pixel value; d) performing head motion correction, using an edge detection-based algorithm to identify and correct head motion; e) performing spatial smoothing on the corrected image data, using a three-dimensional Gaussian kernel for convolution operations.

[0007] Preferably, the dynamic image fusion and analysis module further comprises the following steps: a) receiving standardized image data from a multimodal image data preprocessing module; b) applying a multi-time series analysis method, using a time series analysis or a recursive neural network model to generate a postoperative recovery model; c) outputting the generated postoperative recovery model to a personalized efficacy evaluation module.

[0008] Preferably, the personalized efficacy evaluation module further comprises the following steps: a) receiving the postoperative recovery model output by the dynamic image fusion and analysis module; b) combining the clinical characteristic data of the patient and applying a comprehensive evaluation algorithm to generate a personalized postoperative recovery report, and the specific calculation formula is as follows: in , , is the dynamic adjustment coefficient; , is the error correction term; c) outputting the personalized postoperative recovery report to the abnormality detection and intelligent early warning module.

[0009] Preferably, the abnormality detection and intelligent early warning module further comprises the following steps: a) Real-time monitoring of abnormalities in the postoperative recovery report generated by the personalized efficacy evaluation module; b) Application of the error propagation and compensation formula for abnormality detection, the specific formula is as follows: ,in is the error propagation amount, is the error correction coefficient; Control the scope of error propagation; c) Issue early warning notifications based on the test results and transmit the test results to the report generation and visualization module.

[0010] Preferably, the data storage and management module further comprises the following steps: a) Store the original and preprocessed versions of multimodal imaging data; b) Store the postoperative recovery model generated by the dynamic image fusion and analysis module; c) Store the evaluation report and related parameters generated by the personalized efficacy evaluation module; d) Ensure data security and privacy protection, using encrypted storage and access control mechanisms.

[0011] Preferably, the report generation and visualization module further includes the following steps: a) receiving the detection results from the abnormality detection and intelligent early warning module; b) generating a postoperative recovery trend chart and a personalized indicator chart using the evaluation results and the detection data through data visualization technology; c) supporting report output in multiple formats, including PDF, HTML and interactive visualization interface.

[0012] Preferably, the multimodal image data acquisition and preprocessing module further includes the following algorithm steps: a) performing preliminary measurement calibration, the specific calculation formula is as follows: ,in, is the normalized pixel value; is the weight of image preprocessing; is the adjustment factor of environmental parameters; is the environmental parameter; b) Perform secondary precision calibration, the specific calculation formula is as follows: ,in is the dynamic weight of the external parameter; is an external parameter; is the adjustment coefficient, which is used to control the impact of input on output; The function is used for nonlinear correction and smoothing of excessively large input values.

[0013] Preferably, the dynamic image fusion and analysis module further includes the following algorithm steps: a) Use recurrent neural network models to perform time series prediction on multimodal imaging data; b) Applying long short-term memory network structure to capture long-term dependencies in image data; c) Generate a postoperative recovery model as input for personalized efficacy evaluation.

[0014] Preferably, the personalized efficacy evaluation module further includes the following algorithm steps: a) Obtain the patient's clinical characteristics data, including but not limited to age, gender, medical history, and preoperative seizure frequency; b) combining the patient's clinical characteristic data with the postoperative image restoration model to generate a comprehensive evaluation value through a weighted average method; c) Based on the comprehensive evaluation value, the support vector machine classifier is used to predict the patient's postoperative seizure risk; d) Implement phased evaluation, including: i. Generate performance indicators for each phase, calculated as follows: ,in is the performance index of the i-th stage; is the number of samples for each stage; is the evaluation value of the jth sample in the i-th stage; is the reference value of the jth sample; ii. Comprehensive evaluation index, calculated by the following formula: ,in The weight for each stage; e) Implement feedback loops, including: i. Receive accurate calibration results ; ii. Adjust the input data according to the following formula , forming a feedback loop: ,in is the adjusted input data; is the original input data; is the feedback gain coefficient; iii. Adjust the input data Re-input into the multimodal image data acquisition and preprocessing module to dynamically optimize the evaluation results.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides more comprehensive brain structure and function information by integrating various brain imaging data such as T1-weighted images, DTI and fMRI, thereby improving the accuracy of efficacy evaluation.

[0016] The system introduces a feedback loop to dynamically optimize the evaluation results by adjusting the input data, thereby enhancing the adaptability and real-time performance of the system and ensuring the reliability of the evaluation results.

[0017] The present invention implements a staged evaluation and obtains a final comprehensive evaluation index through the weighted sum of performance indicators at each stage, thereby achieving a comprehensive analysis of multi-level data and improving the accuracy and consistency of the evaluation.

[0018] Through the error propagation and compensation model, the system can effectively adjust the difference between the initial calibration results and the reference value, minimize the error in the evaluation process, and improve the credibility of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the overall architecture diagram of the system of the present invention; Figure 2 This is a flow chart of multimodal image data acquisition and preprocessing of the present invention; Figure 3 This is a workflow diagram of the dynamic image fusion and analysis module of the present invention; Figure 4 This is a workflow diagram of the personalized efficacy evaluation module of the present invention; Figure 5 This is a working diagram of the abnormality detection and intelligent early warning module of the present invention; Figure 6 This is a schematic diagram of the data storage and management module architecture of the present invention; Figure 7 The report generation and visualization module workflow diagram of the present invention; Figure 8 This is the initial measurement calibration and secondary precision calibration process of the present invention; Fig. 9 The structure diagram of the recursive neural network and the long short-term memory network of the present invention; Fig.10 This is a feedback loop mechanism diagram of the personalized efficacy evaluation module of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figures 1 to 10 The present invention provides a temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features. The system aims to accurately and comprehensively evaluate the efficacy of temporal lobe epilepsy resection surgery by integrating multimodal brain image data and using advanced algorithm models. The system is mainly composed of the following six modules: multimodal image data acquisition and preprocessing module; dynamic image fusion and analysis module; personalized efficacy evaluation module; abnormality detection and intelligent early warning module; data storage and management module; report generation and visualization module.

[0022] Among them, the multimodal imaging data acquisition and preprocessing module is responsible for collecting different types of brain imaging data obtained by patients before and after surgery, and performing a series of preprocessing steps on these data to ensure data quality and consistency. The core task of this module is to convert the original imaging data into a standardized format suitable for subsequent analysis and eliminate noise and motion artifacts that may affect the evaluation results; its specific implementation steps include: 1. Data acquisition: T1-weighted images: provide high-resolution images of brain structures, which can clearly show the anatomical details of brain tissue. Diffusion Tensor Imaging (DTI): used to analyze the direction and integrity of white matter fiber bundles and evaluate changes in nerve fibers. Functional Magnetic Resonance Imaging (fMRI): monitor brain activity and evaluate postoperative functional recovery.

[0023] 2. Data preprocessing: Data format conversion: Convert the raw image data of different modalities into a unified standard format (such as NIfTI format) for subsequent processing and analysis. Denoising: Use wavelet transform (WaveletTransform) or Gaussian filtering (Gaussian Filtering) method to remove random noise in the image and improve image quality. Wavelet transform: Through multi-scale decomposition, effectively remove noise in different frequency ranges. Gaussian filtering: Use Gaussian kernel to smooth the image and reduce high-frequency noise. Standardization: Calculate the mean of the image data and standard deviation , and apply the normalization formula: ,in is the mean of the image data, is the standard deviation of the image data, is the standardized pixel value; this standardization step ensures the comparability of imaging data between different patients and eliminates the impact of individual differences.

[0024] Head motion correction: Use edge detection-based algorithms to identify and correct artifacts caused by patient head motion during image acquisition to ensure geometric consistency of the image. Edge detection algorithm: Detect the degree of head motion by identifying edge features in the image and make corresponding corrections. Spatial smoothing: Use a three-dimensional Gaussian kernel for convolution operations to perform spatial smoothing on the corrected image data to further improve image quality and reduce residual noise and artifacts.

[0025] Multimodal data integration: By integrating T1-weighted images, DTI and fMRI data, the patient's brain structure and functional status are fully reflected, providing a rich information basis for subsequent evaluation. Efficient preprocessing process: De-noising, standardization, head motion correction and spatial smoothing methods are used to significantly improve the quality and consistency of image data and ensure the accuracy and reliability of evaluation results. Parameters are clear and traceable: Parameters in all preprocessing steps (such as mean and standard deviation ) have clear sources and calculation methods to ensure the rationality and traceability of the calculation process.

[0026] Furthermore, the dynamic image fusion and analysis module is responsible for receiving the pre-processed multimodal image data and applying the multi-time series analysis method to generate a postoperative recovery model. The core of this module is to conduct in-depth analysis of image data through advanced neural network algorithms (recursive neural network RNN ​​and long short-term memory network LSTM) to capture the dynamic changes in the postoperative recovery process. The specific implementation steps include: 1. Data reception and fusion: Receiving preprocessed data: The dynamic image fusion and analysis module receives standardized T1-weighted images, DTI and fMRI data from the multimodal image data acquisition and preprocessing module. Data fusion: Align the image data of different modalities in space and time and integrate them into a unified multidimensional data set for subsequent analysis and modeling.

[0027] 2. Application of multiple time series analysis methods: Recurrent Neural Network (RNN): Suitable for processing sequence data and can capture the time dependency in the data. Through cyclic connection, RNN can retain the information of the previous step at each step of the sequence, which is suitable for analyzing the time series changes of image data. Long Short-Term Memory Network (LSTM): It is a special RNN structure that can better capture long-term dependencies and avoid the gradient vanishing problem in traditional RNN. LSTM can selectively remember or forget information by introducing forget gate, input gate and output gate, which is suitable for complex time series prediction tasks.

[0028] 3. Postoperative recovery model generation: Model training and prediction: Use the pre-processed multimodal imaging data to train the RNN or LSTM model to learn the dynamic change pattern of the patient's postoperative recovery process. Recovery model output: The generated postoperative recovery model can reflect the change trend of the patient's brain structure and function at different time points after surgery, providing a basis for personalized efficacy evaluation.

[0029] Furthermore, the personalized efficacy evaluation module aims to combine the postoperative recovery model generated by the dynamic image fusion and analysis module with the patient's clinical characteristic data to conduct a comprehensive analysis and generate a personalized postoperative recovery report. By applying a comprehensive evaluation algorithm, this module can provide accurate efficacy evaluation based on the patient's specific situation and predict the risk of postoperative seizures. Its specific implementation steps include: 1. Data reception and integration: Postoperative recovery model reception: The personalized efficacy evaluation module receives the postoperative recovery model generated from the dynamic image fusion and analysis module, which reflects the changes in the patient's brain structure and function at different time points after surgery; Clinical characteristic data acquisition: Collect the patient's clinical characteristic data, including but not limited to age, gender, medical history, preoperative seizure frequency, etc. These data are obtained through the electronic medical record system or manual input, and the accuracy and completeness of the data are ensured.

[0030] 2. Application of comprehensive evaluation algorithm: The following comprehensive evaluation formula is used to generate a personalized postoperative recovery report: in , , is the dynamic adjustment coefficient; , is the error correction term; It is a comprehensive evaluation value, representing the overall efficacy evaluation result; To provide preliminary measurement and calibration results, reflecting basic evaluation indicators; To accurately calibrate the results, reflect the further optimized evaluation indicators; It is to perform logarithmic transformation on the precise calibration results, smooth the evaluation values, and reduce the impact of extreme values.

[0031] 3. Seizure risk prediction: Support vector machine (SVM) classifier application: based on comprehensive evaluation value Use a pre-trained SVM classifier to predict the patient's risk of postoperative seizures. The SVM classifier determines the patient's risk level of seizures (such as high risk, medium risk, and low risk) by analyzing the relationship between the comprehensive evaluation value and historical data. Prediction result integration: Integrate the prediction results of the SVM classifier into a personalized postoperative recovery report and provide it to the medical team as a reference for decision-making; 4. Report generation and delivery: Report generation: Based on the above analysis results, a detailed personalized postoperative recovery report is generated, including comprehensive evaluation values, seizure risk prediction results and corresponding recommended measures. Report delivery: The generated personalized postoperative recovery report is delivered to the abnormal detection and intelligent early warning module as a basis for further monitoring and optimization.

[0032] Furthermore, the anomaly detection and intelligent early warning module is responsible for real-time monitoring of abnormalities in the postoperative recovery report generated by the personalized efficacy evaluation module, implementing error compensation, and issuing early warning notifications. This module ensures the accuracy and reliability of the evaluation results through the error propagation and compensation mechanism, and promptly notifies the medical team of possible problems through the early warning mechanism. Its specific implementation steps include: 1. Abnormal monitoring: Real-time data reception: Receive the postoperative recovery report generated from the personalized efficacy evaluation module and monitor the various evaluation indicators in the report. Abnormal detection: Analyze the received evaluation indicators in real time to identify situations that are beyond the normal range or abnormal changes. Determine which evaluation results need attention through preset thresholds and rules.

[0033] 2. Error propagation and compensation: Application of error propagation formula: ,in is the error propagation amount, is the error correction coefficient, which reflects the influence of different error sources; The parameters that control the range of error propagation determine the speed at which the error decays; is the reference value, usually historical data or standard value. Error compensation implementation: calculated by the above formula , adjust the preliminary assessment results, compensate for possible errors, and ensure the accuracy of the assessment results.

[0034] 3. Warning notification: Warning trigger condition setting: According to the error propagation and abnormal detection results, set the warning trigger conditions. For example, when When a certain threshold is exceeded or an evaluation indicator deviates significantly from the reference value, an early warning is triggered. Early warning notification generation: Once the early warning condition is triggered, the system automatically generates an early warning notification, which includes a description of the abnormal situation, possible cause analysis, and recommended countermeasures. Notification delivery: The generated early warning notification is sent to the report generation and visualization module, further displayed to the medical team, and immediately notified via SMS, email, etc.

[0035] Furthermore, the data storage and management module is responsible for the storage and management of all imaging data, analysis results and patient information in the system, ensuring data security and privacy protection. This module ensures data integrity and accessibility through efficient data management strategies and security mechanisms, supports the stable operation of the system and efficient use of data. Its specific implementation steps include: 1. Data storage: Original data storage: Stores the original version of multimodal imaging data to ensure data integrity and traceability. Including preoperative and postoperative T1-weighted images, DTI and fMRI data. Preprocessing data storage: Stores image data preprocessed by the multimodal imaging data acquisition and preprocessing module to facilitate subsequent analysis and use. Analysis result storage: Stores the postoperative recovery model generated by the dynamic image fusion and analysis module, the evaluation report generated by the personalized efficacy evaluation module, and related parameters.

[0036] 2. Data management: Classification and indexing: Classify and index the stored data according to patient ID, image type, time point and other information to ensure fast retrieval and efficient management of data. Backup and recovery: Back up all data regularly and establish a data recovery mechanism to prevent data loss or damage.

[0037] 3. Data security and privacy protection: Encrypted storage: All stored data is encrypted using a high-strength encryption algorithm (such as AES-256) to ensure data security during storage and transmission. Access control: Implement a strict access control mechanism so that only authorized personnel can access sensitive data. Prevent unauthorized access and data leakage through user authentication and permission management. Logging: Record all data access and operation logs for easy auditing and tracking, ensuring transparency and traceability of data operations.

[0038] Furthermore, the report generation and visualization module is responsible for integrating the data generated by the personalized efficacy evaluation module and the anomaly detection and intelligent early warning module, and generating reports that are easy to understand and analyze through visualization technology. These reports include postoperative recovery trend charts and personalized indicator charts, aiming to provide the medical team with intuitive data presentation to assist them in making clinical decisions. The specific implementation steps include: 1. Data reception and integration: Receive test results: Receive test results from the abnormality detection and intelligent early warning module, which include abnormal conditions and related data during the evaluation process. Receive evaluation data: Receive the generated personalized postoperative recovery report from the personalized efficacy evaluation module, including comprehensive evaluation values ​​and seizure risk prediction results. Data integration: Integrate the data from the two modules to ensure that all relevant information can fully reflect the patient's postoperative recovery when generating the report.

[0039] 2. Data visualization: Recovery trend chart generation: Use data visualization tools (such as Matplotlib or D3.js) to generate charts showing the postoperative recovery trend. The recovery trend chart shows the changes in the patient's brain structure and function at different time points, helping the medical team to intuitively understand the recovery process. Personalized indicator chart generation: Based on the results of personalized efficacy evaluation, generate charts showing individualized indicators (such as comprehensive evaluation values, seizure risk level, etc.). These indicator charts can highlight the patient's specific recovery status and potential risks, providing support for precision medicine.

[0040] 3. Report output and delivery: Multi-format support: The system supports the generation of reports in multiple formats, including PDF, HTML, and interactive visualization interfaces. PDF format is suitable for printing and archiving, HTML format is convenient for online viewing, and the interactive interface supports the medical team to dynamically analyze and explore the data. Report delivery: Send the generated visualization report to the medical team's workstation or share it through a secure network platform to ensure that medical staff can obtain and view the evaluation results in a timely manner to assist them in making clinical decisions.

[0041] Furthermore, the multimodal image data acquisition and preprocessing module is not only responsible for basic data acquisition and preprocessing, but also further performs preliminary measurement calibration and precision calibration to ensure high accuracy and consistency of image data. These algorithm steps further improve the reliability of image data in subsequent analysis through a refined calibration process. The specific implementation steps include: 1. Preliminary measurement and calibration: Formula application: ,in, is the normalized pixel value; is the weight of image preprocessing; is the adjustment factor of environmental parameters; is the environmental parameter; by giving different pixel values ​​different weights , the system can highlight important areas and reduce the impact of irrelevant areas; by adjusting the coefficient and environmental parameters , the system can compensate for the impact of external environmental changes on image data and ensure the stability and consistency of the evaluation results.

[0042] 2. Secondary precision calibration: Formula application: ,in is the dynamic weight of the external parameter; is an external parameter; is the adjustment coefficient, which is used to control the impact of input on output; The function is used for nonlinear correction and smoothing of excessive input values; pass Dynamically adjust the weights of different external parameters, the system can flexibly respond to various external interferences according to actual conditions; use The function realizes nonlinear correction, ensuring that the calibration process can effectively compensate for errors without causing new errors due to over-adjustment; Accuracy improvement: The secondary precision calibration further improves the accuracy and reliability of the evaluation results by comprehensively considering multiple external factors.

[0043] Furthermore, the dynamic image fusion and analysis module further uses recurrent neural network (RNN) and long short-term memory network (LSTM) structures to predict time series and capture long-term dependencies of multimodal image data. Through these advanced deep learning algorithms, the module can generate a more accurate and comprehensive postoperative recovery model. The specific implementation steps include: 1. Time series prediction: Recurrent neural network (RNN) model application: Model structure: RNN is a neural network structure suitable for processing sequence data, which can capture the time dependency in the data. Working principle: RNN maintains a hidden state through cyclic connection, and combines the hidden state of the previous step with the current input at each time step to generate a new hidden state and output.

[0044] Implementation steps: Data input: Input the preprocessed multimodal imaging data as a time series into the RNN model. Hidden state update: At each time step, the hidden state is updated according to the current input and the previous hidden state. Output generation: Generate the postoperative recovery index for the corresponding time step, reflecting the patient's recovery status at that time point. RNN can remember the information of the previous time step through its cyclic structure, which is suitable for analyzing the continuous changes in the patient's postoperative recovery process. This structure enables the model to identify and utilize long-term dependencies in imaging data to improve the accuracy of prediction.

[0045] 2. Capturing long-term dependencies: Long short-term memory network (LSTM) structure application: Model structure: LSTM is a special RNN structure designed to solve the gradient vanishing problem of traditional RNN when dealing with long-term dependencies. Working principle: LSTM can selectively retain or forget information by introducing forget gate, input gate and output gate, thereby effectively capturing long-term dependencies.

[0046] Implementation steps: Data input: Input the preprocessed multimodal imaging data into the LSTM model. Application of gating mechanism: Control the flow of information through the forget gate, input gate and output gate, selectively retain important information and ignore irrelevant information. Recovery model generation: Generate a more accurate and stable postoperative recovery model to reflect the dynamic changes of patients in the long-term recovery process. LSTM can effectively process and memorize data changes over a long period of time through its gating mechanism, and is suitable for analyzing the long-term recovery trend of patients after surgery. This structure enables the model to maintain efficient and accurate prediction capabilities when facing complex and long-term series data.

[0047] 3. Restore model output: Model training and validation: Data segmentation: Divide the collected multimodal imaging data into training set and validation set to ensure the generalization ability of the model. Model training: Use the training set to train the RNN and LSTM models, and optimize the model parameters by minimizing the prediction error. Model validation: Evaluate the model performance on the validation set to ensure its prediction accuracy and stability on unseen data.

[0048] Recovery model generation: Model application: Apply the trained RNN or LSTM model to the multimodal imaging data of new patients to generate a personalized postoperative recovery model. Result output: The recovery model includes structural and functional evaluation indicators at different time points, providing accurate data support for the personalized efficacy evaluation module.

[0049] Furthermore, the personalized efficacy evaluation module further includes obtaining patient clinical characteristic data, generating comprehensive evaluation values, predicting postoperative seizure risk, staged evaluation, and feedback loop mechanism. These steps achieve a comprehensive and personalized evaluation of the patient's postoperative recovery by comprehensively considering the patient's clinical characteristics and image recovery model. The specific implementation steps include: 1. Obtain patient clinical characteristic data: Data collection: Collect the patient's basic information and clinical characteristic data, including but not limited to age, gender, medical history, preoperative seizure frequency, etc. These data are obtained through the electronic medical record system, questionnaire survey or manual input. Data integration: Integrate the collected clinical characteristic data with the postoperative recovery model generated by the dynamic image fusion and analysis module to provide a comprehensive data basis for comprehensive evaluation.

[0050] 2. Generate comprehensive evaluation value: Application of weighted average method: in , , is the dynamic adjustment coefficient; , is the error correction term; It is a comprehensive evaluation value, representing the overall efficacy evaluation result; To provide preliminary measurement and calibration results, reflecting basic evaluation indicators; To accurately calibrate the results, reflect the further optimized evaluation indicators; It is to perform logarithmic transformation on the precise calibration results, smooth the evaluation values, and reduce the impact of extreme values.

[0051] The comprehensive evaluation value is obtained by weighted combination of preliminary evaluation results and accurate evaluation results , comprehensively reflects the patient's postoperative recovery status; , , The introduction of the evaluation process enables the evaluation process to be flexibly adjusted according to the specific conditions and needs of different patients, ensuring the personalization and accuracy of the evaluation results; , Compensate for errors in the evaluation results, reduce the impact of systematic errors on the final evaluation value, and improve the credibility of the evaluation results; The application of can effectively smooth the evaluation values, avoid the excessive impact of extreme values ​​on the comprehensive evaluation results, and improve the stability of the evaluation.

[0052] 3. Postoperative seizure risk prediction: Support vector machine (SVM) classifier application: Model training: Use historical patient data (including comprehensive assessment values ​​and postoperative seizure conditions) to train the SVM classifier and establish an association model between the comprehensive assessment value and the seizure risk. Model application: Based on the comprehensive assessment value of the current patient , use the trained SVM classifier to predict the risk level of postoperative seizures (such as high risk, medium risk, low risk). Prediction result integration: Integrate the predicted risk level of seizures into the personalized postoperative recovery report to provide early warning and intervention suggestions for the medical team.

[0053] The SVM classifier has good classification performance, can effectively process high-dimensional data, and is suitable for complex seizure risk prediction tasks. Through training and validation, the SVM model can accurately distinguish different risk levels and improve the accuracy and reliability of prediction.

[0054] 4. Staged evaluation: Generate performance indicators at each stage: ,in is the performance indicator of the i-th stage, measuring the difference between the evaluation result of this stage and the reference value; is the number of samples for each stage; is the evaluation value of the jth sample in the i-th stage; is the reference value of the jth sample; calculation of comprehensive evaluation index: ,in The weight of each stage reflects the importance of different stages in the comprehensive evaluation; It is a comprehensive evaluation indicator, representing the overall efficacy evaluation result.

[0055] By calculating the performance indicators of each stage, the system can carefully evaluate the effects of different recovery stages and identify potential problems; The setting ensures that the evaluation results at different stages occupy corresponding proportions in the comprehensive evaluation according to their importance, thereby improving the comprehensiveness and accuracy of the evaluation results.

[0056] 5. Feedback loop mechanism: Receive accurate calibration results: Receive accurate calibration results from the second precision calibration step ; Adjust input data to form a feedback loop: ,in is the adjusted input data; is the original input data; is the feedback gain coefficient, It is the derivative of the precise calibration result with respect to the input data, indicating the degree of influence of the input data change on the evaluation result.

[0057] Input data reprocessing: Adjust the input data Re-input into the multimodal image data acquisition and preprocessing module to dynamically optimize the evaluation results; through the feedback loop mechanism, the system can dynamically adjust the input data according to the precise calibration results, optimize the evaluation process in real time, and improve the system's adaptability and the accuracy of the evaluation results. The settings ensure the appropriateness of input data adjustment, avoiding over-adjustment that may lead to system instability or distorted evaluation results. All parameters and steps in the feedback adjustment process are clearly defined to ensure the transparency and traceability of the adjustment process.

[0058] In summary, the present invention realizes a comprehensive, accurate and dynamic evaluation of the efficacy of temporal lobe epilepsy resection surgery through the collaborative work of multimodal image data acquisition and preprocessing module, dynamic image fusion and analysis module, personalized efficacy evaluation module, abnormal detection and intelligent early warning module, data storage and management module, and report generation and visualization module. The system integrates a variety of brain imaging data, applies advanced deep learning algorithms, and combines personalized clinical characteristics to provide highly accurate and reliable evaluation results. At the same time, through error compensation and feedback loop mechanisms, the system has the ability of real-time optimization and dynamic adjustment to ensure the stability of the evaluation process and the credibility of the results. The data storage and management module ensures the security and privacy protection of the data, and the report generation and visualization module provides the medical team with intuitive data display to assist them in making clinical decisions. The overall system not only improves the accuracy and reliability of efficacy evaluation, but also significantly improves the quality of life of patients with temporal lobe epilepsy after surgery, and has broad clinical application prospects.

[0059] In order to further illustrate the specific implementation of the present invention, the implementation process and advantages of the present invention in different application scenarios are detailed below through specific examples. These examples will help to more intuitively understand the working principle and practical application effect of the system and verify the practicality and superiority of the present invention in a clinical environment.

[0060] Example 1: Postoperative evaluation of temporal lobe epilepsy in a standard clinical setting Temporal lobe epilepsy is one of the most common types of drug-resistant epilepsy, and surgical resection is one of the effective treatments. However, postoperative efficacy evaluation usually relies on clinical symptom observation and single-modality imaging data analysis, which is difficult to fully reflect the changes in brain structure and function, resulting in insufficient accuracy and reliability of the evaluation.

[0061] This embodiment aims to integrate multimodal brain imaging data through the temporal lobe epilepsy resection surgery efficacy evaluation system of the present invention, use advanced algorithm models, provide a comprehensive and accurate postoperative recovery evaluation, improve the accuracy and reliability of the evaluation, and assist the medical team in making clinical decisions. Implementation

[0062] Patient data collection: T1-weighted images, DTI and fMRI data of patients were collected before and after surgery.

[0063] Data preprocessing: Use the multimodal image data acquisition and preprocessing module to perform denoising, standardization, head motion correction and spatial smoothing on the collected data.

[0064] Image fusion and analysis: The dynamic image fusion and analysis module receives the preprocessed data and applies the RNN and LSTM models to generate a postoperative recovery model.

[0065] Personalized evaluation: The personalized efficacy evaluation module combines the recovery model and the patient's clinical characteristic data to generate a comprehensive evaluation value and postoperative relapse risk prediction.

[0066] Anomaly monitoring and early warning: The anomaly detection and intelligent early warning module monitors the evaluation results in real time, compensates for errors, and issues early warning notifications when anomalies are found.

[0067] Report Generation and Visualization: The Report Generation and Visualization module integrates assessment and test data to generate multi-format visual reports for the medical team to review and make decisions.

[0068] Multimodal data integration: By integrating T1-weighted images, DTI and fMRI data, it can comprehensively reflect the changes in brain structure and function.

[0069] Advanced deep learning algorithms: Using RNN and LSTM models to accurately capture the dynamic changes of postoperative recovery.

[0070] Personalized assessment and risk prediction: Combined with clinical characteristic data, it provides personalized efficacy evaluation and onset risk prediction.

[0071] Real-time anomaly monitoring and early warning: Ensure the accuracy and reliability of evaluation results through error compensation and early warning mechanisms.

[0072] In a standard clinical setting, this system is used to conduct postoperative recovery assessment for patients undergoing temporal lobe epilepsy resection surgery, assisting physicians in developing personalized postoperative management and intervention plans.

[0073] By implementing this system, we can more comprehensively and accurately evaluate the patient's postoperative recovery, detect abnormalities in a timely manner, improve the accuracy and reliability of efficacy evaluation, assist the medical team in making scientific decisions, and significantly improve the patient's postoperative quality of life.

[0074] Comparative experiment: In order to verify the superiority of the system of the present invention, a comparative experiment with the existing single-modality image evaluation method was carried out.

[0075]

[0076] Comparative results analysis: Assessment accuracy and reliability: The system of the present invention significantly improves the assessment accuracy and reliability through multimodal data integration and advanced algorithms. Data integration capability: Compared with single-modal methods, this system can integrate a variety of brain imaging data to provide a more comprehensive assessment basis. Ability to capture time dependencies: Using RNN and LSTM models, the system can effectively capture and utilize time dependencies in the data to improve prediction capabilities. Risk prediction capability: The accuracy of seizure risk prediction of this system is higher than that of existing methods, providing more reliable clinical decision support. Timeliness of anomaly detection: Real-time monitoring and early warning mechanisms enable the system to respond quickly to abnormal situations and reduce potential risks. Data security: High-strength encryption and strict access control ensure the security and privacy protection of patient data. User-friendliness: Multi-format visual reports enable the medical team to understand the assessment results more intuitively, improving the practicality and ease of use of the system.

[0077] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features, characterized in that: The system includes the following modules: a) Multimodal imaging data acquisition and preprocessing module, responsible for acquiring and preprocessing preoperative and postoperative T1-weighted images, diffusion tensor imaging, and functional magnetic resonance imaging data. The preprocessing steps include denoising, standardization, head motion correction, and spatial smoothing. b) a dynamic image fusion and analysis module, which receives the image data preprocessed by the multimodal image data acquisition and preprocessing module, generates a postoperative recovery model using a multi-time series analysis method, and transmits the postoperative recovery model to the personalized efficacy evaluation module; c) A personalized efficacy evaluation module combines the postoperative recovery model and patient characteristic data generated by the dynamic image fusion and analysis module to analyze and generate a personalized postoperative recovery report, which is then transmitted to the abnormality detection and intelligent early warning module; d) Abnormal detection and intelligent early warning module, which monitors abnormal conditions in the postoperative recovery report generated by the personalized efficacy evaluation module in real time, implements error compensation, issues early warning notifications, and transmits the detection results to the report generation and visualization module; e) Data storage and management module, responsible for storing and managing multimodal imaging data, analysis results and patient information; f) Report generation and visualization module, which receives data transmitted by the personalized efficacy evaluation module and the anomaly detection and intelligent early warning module, and generates a visualization report showing the postoperative recovery trend and personalized indicators.

2. According to claim 1, a temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features is characterized by: The multimodal image data acquisition and preprocessing module further comprises the following steps: a) Convert the raw image data of preoperative and postoperative T1-weighted images, diffusion tensor imaging, and functional magnetic resonance imaging into a standard format; b) De-noising the converted image data by using wavelet transform or Gaussian filtering; c) Standardize the denoised image data and calculate the mean of the image data and standard deviation , and apply the normalization formula: ,in is the mean of the image data, is the standard deviation of the image data, is the normalized pixel value; d) Perform head motion correction, using an edge detection-based algorithm to identify and correct head motion; e) Perform spatial smoothing on the corrected image data and perform convolution operation using a three-dimensional Gaussian kernel.

3. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The dynamic image fusion and analysis module further comprises the following steps: a) receiving standardized image data from a multimodal image data preprocessing module; b) Applying multiple time series analysis methods, using time series analysis or recurrent neural network models to generate postoperative recovery models; c) Output the generated postoperative recovery model to the personalized efficacy evaluation module.

4. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The personalized efficacy assessment module further includes the following steps: a) receiving the postoperative recovery model output by the dynamic image fusion and analysis module; b) Combined with the patient's clinical characteristics data, a comprehensive evaluation algorithm is used to generate a personalized postoperative recovery report. The specific calculation formula is as follows: in , , is the dynamic adjustment coefficient; , is the error correction term; c) outputting the personalized postoperative recovery report to the abnormality detection and intelligent early warning module.

5. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The abnormality detection and intelligent early warning module further comprises the following steps: a) Real-time monitoring of abnormalities in the postoperative recovery report generated by the personalized efficacy evaluation module; b) Apply the error propagation and compensation formula for anomaly detection. The specific formula is as follows: ,in is the error propagation amount, is the error correction coefficient; Control the scope of error propagation; c) Issue early warning notifications based on the test results and transmit the test results to the report generation and visualization module.

6. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The data storage and management module further comprises the following steps: a) storing raw and pre-processed versions of multimodal imaging data; b) storing the postoperative recovery model generated by the dynamic image fusion and analysis module; c) storing the evaluation report and related parameters generated by the personalized efficacy evaluation module; d) Ensure data security and privacy protection by adopting encrypted storage and access control mechanisms.

7. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The report generation and visualization module further comprises the following steps: a) Receive detection results from the anomaly detection and intelligent early warning module; b) Generate postoperative recovery trend charts and personalized indicator charts using data visualization technology based on the evaluation results and test data; c) Supports report output in multiple formats, including PDF, HTML, and interactive visualization interface.

8. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The multimodal image data acquisition and preprocessing module further includes the following algorithm steps: a) performing preliminary measurement calibration, the specific calculation formula is as follows: ,in, is the normalized pixel value; is the weight of image preprocessing; is the adjustment factor of environmental parameters; is the environmental parameter; b) Perform secondary precision calibration, the specific calculation formula is as follows: ,in is the dynamic weight of the external parameter; is an external parameter; is the adjustment coefficient, which is used to control the impact of input on output; The function is used for nonlinear correction and smoothing of excessively large input values.

9. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The dynamic image fusion and analysis module further includes the following algorithm steps: a) Use recurrent neural network models to perform time series prediction on multimodal imaging data; b) Applying long short-term memory network structure to capture long-term dependencies in image data; c) Generate a postoperative recovery model as input for personalized efficacy evaluation.

10. The temporal lobe epilepsy resection surgery efficacy evaluation system based on brain image fusion features according to claim 1, characterized in that: The personalized efficacy evaluation module further includes the following algorithm steps: a) Obtain the patient's clinical characteristics data, including but not limited to age, gender, medical history, and preoperative seizure frequency; b) combining the patient's clinical characteristic data with the postoperative image restoration model to generate a comprehensive evaluation value through a weighted average method; c) Based on the comprehensive evaluation value, the support vector machine classifier is used to predict the patient's postoperative seizure risk; d) Implement phased evaluation, including: i. Generate performance indicators for each phase, calculated as follows: ,in is the performance index of the i-th stage; is the number of samples for each stage; is the evaluation value of the jth sample in the i-th stage; is the reference value of the jth sample; ii. Comprehensive evaluation index, calculated by the following formula: ,in The weight for each stage; e) Implement feedback loops, including: i. Receive accurate calibration results ; ii. Adjust the input data according to the following formula , forming a feedback loop: ,in is the adjusted input data; is the original input data; is the feedback gain coefficient; iii. Adjust the input data Re-input into the multimodal image data acquisition and preprocessing module to dynamically optimize the evaluation results.

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