Accurate tumor intervention ablation assessment feedback system

Abstract

The invention discloses a precise tumor interventional ablation assessment feedback system. The system comprises the following steps: step 1, configuring system hardware; step 2, data acquisition and preprocessing; 3, constructing a deep learning model; 4, real-time image data fusion is carried out; 5, monitoring and optimizing the ablation process; and step 6, effect evaluation and feedback. By means of the real-time multi-modal image fusion technology, a doctor can know the state change of a tumor in real time in the ablation process, according to the scheme, by combining biological feedback and automatic quantitative analysis, objective data support can be provided, the influence of subjective judgment can be reduced, and therefore evaluation of the ablation effect is more accurate and reliable. In combination with real-time images and physiological feedback, the system can provide an individualized ablation scheme for each patient, it is ensured that the treatment scheme better conforms to the specific situation of the patient, and the overall treatment effect and safety of the patient can be improved through the individualized treatment strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor ablation evaluation, and particularly to a precise tumor interventional ablation evaluation and feedback system. Background Art

[0002] Precise tumor interventional ablation technology has received extensive attention in recent years and has become an important direction in tumor treatment. This technology effectively destroys tumor tissue by applying means such as thermal energy, cold energy, or chemical agents to achieve the purpose of eliminating tumors. Common ablation methods include radiofrequency ablation, microwave ablation, cryoablation, etc. With the progress of medical imaging technologies such as CT, MRI, and ultrasound, medical professionals can monitor the specific location of tumors and the ablation process in real time, thereby improving the accuracy of treatment. However, existing evaluation and feedback systems still have limitations in real-time data integration, ablation effect evaluation, and the formulation of personalized treatment plans for patients. The lack of an effective feedback mechanism makes it difficult to achieve dynamic adjustment during the treatment process, which may lead to poor ablation effects or increased side effects. Therefore, developing an innovative precise tumor interventional ablation evaluation and feedback system to enhance the real-time, precision, and personalization of treatment has become an urgent problem to be solved in the current medical field.

[0003] However, there are still significant deficiencies in the existing technology, such as:

[0004] In the existing technology, there is a lack of a real-time feedback mechanism during the tumor ablation process, resulting in doctors being unable to obtain information on the changes in tumor status in a timely manner during operation, thus affecting the ablation effect. Traditional ablation effect evaluation often relies on doctors' experience judgment and lacks quantitative analysis, which easily leads to inconsistent and misjudged evaluation results. Summary of the Invention

[0005] The purpose of the present invention is to provide a precise tumor interventional ablation evaluation and feedback system to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A precise tumor interventional ablation evaluation and feedback system includes the following steps:

[0008] Step 1: System hardware configuration;

[0009] Step 2: Data acquisition and preprocessing;

[0010] Step 3: Construction of a deep learning model;

[0011] Step 4: Real-time image data fusion;

[0012] Step 5: Ablation process monitoring and optimization;

[0013] Step Six: Effect Evaluation and Feedback.

[0014] Preferably, the Step One: System Hardware Configuration specifically includes:

[0015] Device Selection:

[0016] Select high-resolution CT and MRI devices to ensure high-quality image data can be provided, and equip with high-frequency ultrasound devices for facilitating real-time monitoring of tumor ablation effects;

[0017] Computing Platform Setup:

[0018] Configure a high-performance GPU server to ensure that deep learning algorithms can operate efficiently during real-time processing, and ensure that the system has sufficient storage capacity for storing a large amount of image data and training models.

[0019] Preferably, the Step Two: Data Acquisition and Preprocessing specifically includes:

[0020] Initial Data Acquisition:

[0021] Perform multi-modal image data acquisition of CT, MRI, and ultrasound before surgery, and the acquired data should include the location information and morphological characteristics of the tumor;

[0022] Data Preprocessing:

[0023] Clean and standardize the data, including image denoising, alignment, and resampling, so that data from different imaging sources can be processed under the same standard, and use image enhancement technology to improve image quality to ensure effectiveness during subsequent processing.

[0024] Preferably, the Step Three: Deep Learning Model Construction specifically includes:

[0025] Model Selection:

[0026] Adopt the U-Net structure, which has good image segmentation performance;

[0027] Model Training:

[0028] Use the processed multi-modal dataset for model training, label the tumor area as the training target, and adopt cross-validation technology to optimize model parameters to ensure the generalization ability of the model.

[0029] Preferably, the Step Four: Real-time Image Data Fusion specifically includes:

[0030] Implementation of Image Fusion Algorithm:

[0031] Implement a real-time image fusion algorithm based on deep learning to synthesize the image data from CT, MRI, and ultrasound to generate a high-resolution composite image;

[0032] Real-time feedback mechanism:

[0033] During the ablation process, the tumor status is monitored in real time, and the system automatically updates and displays the latest fused images, providing real-time feedback to the operating doctor.

[0034] Preferably, step five: Monitoring and optimization of the ablation process specifically includes:

[0035] Dynamic monitoring:

[0036] Configure a real-time monitoring system, combine biological signals, and monitor the patient's status at any time during the ablation process;

[0037] Precisely adjust ablation parameters:

[0038] According to the real-time feedback of the fused images and biological signals, dynamically adjust the power and time of the ablation device to ensure the efficiency and safety of the ablation process.

[0039] Preferably, step six: Effect evaluation and feedback specifically includes:

[0040] Ablation effect evaluation:

[0041] After the ablation is completed, use the fused images for effect evaluation, identify the ablation area and tumor residue, and quantify the ablation effect using an algorithm;

[0042] Generate a report:

[0043] Automatically generate a detailed ablation effect report, including the ablation area, tumor changes, and patient physiological feedback data, for reference in subsequent medical decisions.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] 1. Through the real-time multimodal image fusion technology, doctors can understand the status changes of tumors in real time during the ablation process, thereby dynamically adjusting the ablation parameters according to the latest imaging data, significantly improving the accuracy of ablation, and reducing the damage to surrounding healthy tissues;

[0046] 2. By combining biological feedback and automated quantitative analysis, this solution can provide objective data support, reduce the influence of subjective judgment, and thus make the evaluation of ablation effects more accurate and reliable. This scientific evaluation method effectively solves the subjectivity problem of traditional evaluations;

[0047] 3. By combining real-time imaging and physiological feedback, the system can provide an individualized ablation plan for each patient to ensure that the treatment plan is more in line with the specific situation of the patient. This personalized treatment strategy can improve the overall treatment effect and safety of the patient. Description of the Drawings

[0048] Figure 1 This is a schematic diagram of the process of the present invention. Specific embodiments

[0049] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] Please refer to Figure 1 , the present invention provides a technical solution:

[0051] A precise tumor interventional ablation evaluation and feedback system includes the following steps:

[0052] Step 1: System hardware configuration;

[0053] Step 2: Data collection and preprocessing;

[0054] Step 3: Deep learning model construction;

[0055] Step 4: Real-time image data fusion;

[0056] Step 5: Ablation process monitoring and optimization;

[0057] Step 6: Effect evaluation and feedback.

[0058] Step 1: System hardware configuration, specifically including:

[0059] Device selection:

[0060] Select high-resolution CT and MRI devices to ensure that high-quality image data can be provided, and be equipped with high-frequency ultrasound devices to facilitate real-time monitoring of the tumor ablation effect;

[0061] The high-resolution CT scanner can provide detailed three-dimensional images, clearly showing the structure of the tumor and its surrounding tissues, which helps to accurately locate the tumor. The spiral CT technology is adopted to improve the scanning speed and reduce the radiation time received by the patient. The MRI device can provide high-contrast imaging of soft tissues, especially having obvious advantages in the morphology and boundary of tumors. Select an MRI device with functional imaging capabilities such as DWI and fMRI to evaluate the changes in the tumor microenvironment during the ablation process. The high-frequency ultrasound device can provide real-time monitoring, dynamically observing the tumor and its surrounding tissues during the ablation process. Its non-invasiveness and immediacy make it an ideal choice for evaluating the ablation effect and can capture possible complications in real time.

[0062] Computing platform construction:

[0063] Configure high-performance GPU servers to ensure that deep learning algorithms can run efficiently during real-time processing, and ensure that the system has sufficient storage capacity for storing a large amount of image data and training models.

[0064] Equip with multi-card GPUs such as NVIDIA A100 to support the training and real-time inference of deep learning models. Ensure that the GPU has at least 32GB of memory and high computing power to process high-resolution images and complex algorithms. Adopt large-capacity solid-state drives (SSDs) and network-attached storage (NAS) to ensure fast data access and backup capabilities. Combine RAID technology to protect data security and improve read speed to meet the high-frequency data writing and reading requirements.

[0065] Traditional imaging techniques usually use a single imaging modality (such as CT or MRI), while this solution combines multiple high-resolution devices, which can provide more comprehensive and accurate tumor information and improve the accuracy of diagnosis. Existing technologies mainly rely on a single technology for real-time monitoring, while this solution can achieve multi-modal real-time monitoring by equipping high-frequency ultrasound devices, making the evaluation of tumor ablation effects more immediate and effective. When traditional systems process image data, they mostly rely on CPUs, which are slow and inefficient. This solution accelerates the calculation of deep learning models through high-performance GPU servers, making real-time data processing and feedback possible. By combining high-resolution devices and high-performance computing platforms, the system's image acquisition and processing capabilities are significantly improved, ensuring the accuracy and efficiency of real-time processing. The realization of multi-modal image fusion technology depends on high-quality data sources to ensure that doctors can make individualized treatment plans based on comprehensive imaging information.

[0066] Step 2: Data collection and preprocessing, specifically including:

[0067] Initial data collection:

[0068] Perform multi-modal imaging data collection of CT, MRI, and ultrasound before surgery. The collected data should include the location information and morphological characteristics of the tumor.

[0069] Before surgery, perform high-resolution CT scans on the target tumor to ensure detailed three-dimensional images are acquired. Use contrast agents to enhance the scanning effect and provide a clearer view of the tumor and its surrounding tissues. Record key parameters, including the location, size, shape of the tumor, and its relationship with surrounding important structures. Conduct MRI scans, specifically selecting T1-weighted and T2-weighted imaging to obtain detailed soft tissue characteristics of the tumor. Combine functional imaging (such as diffusion-weighted imaging DWI) to evaluate the diffusion characteristics of the tumor and obtain more abundant information. Perform high-frequency ultrasonic detection before ablation to monitor the status of the tumor and its surrounding tissues in real time. Record the dynamic performance of the tumor in ultrasonic imaging to ensure that possible changes during the ablation process can be captured. Store the data in a standard medical imaging format (such as DICOM) to ensure the compatibility of image data generated by different devices. Upload the image data to the central database for subsequent processing and analysis.

[0070] Data preprocessing:

[0071] Clean and standardize the data, including image denoising, alignment, and resampling, so that data from different imaging sources can be processed under the same standard. Utilize image enhancement techniques to improve the image quality and ensure effectiveness during subsequent processing.

[0072] Use image denoising algorithms (such as median filtering, Gaussian filtering, etc.) to remove noise in the images and improve image clarity. For ultrasonic images, specifically process their unique noise to ensure the accuracy of subsequent analysis. Adopt image registration algorithms to spatially align different modality images (CT, MRI, ultrasound) to ensure that different views of the same tumor can accurately correspond. Achieve accurate image alignment through methods such as feature point matching and similarity transformation. Resample the image data to ensure that all modality images are processed at the same spatial resolution (such as a unified voxel size). Linear interpolation or cubic interpolation methods can be used for resampling to minimize image distortion. Apply techniques such as histogram equalization or adaptive histogram equalization (CLAHE) to enhance the contrast of the images, making the distinction between the tumor and surrounding tissues more obvious. Use edge detection algorithms (such as Canny edge detection) to extract the tumor boundary and improve the recognizability of tumor features. Combine multi-modal image data and use image fusion techniques to generate composite images for subsequent training and inference of deep learning models.

[0073] Existing technologies often rely on single-modal images and lack comprehensive information. In contrast, this solution provides more comprehensive tumor feature information by integrating data from CT, MRI, and ultrasound, significantly improving the accuracy of diagnosis. Traditional image processing often lacks systematic standardization steps, resulting in poor data fusion effects for different modalities. This solution ensures the effective combination of data from different sources through a standardized process of cleaning, alignment, and resampling, enhancing the effectiveness of subsequent analysis. In existing technologies, the clarity and contrast of images are often insufficient. However, through the application of image enhancement technology, the quality of images can be significantly improved, making tumor features more clearly distinguishable.

[0074] Through the comprehensive acquisition of multi-modal images, this step ensures that the system can obtain comprehensive and detailed tumor information, providing a solid foundation for subsequent ablation procedures. Data cleaning and standardization processing ensure that different modality data are processed under the same standard, improving data consistency and compatibility, which is conducive to subsequent analysis and the training of deep learning models. The preprocessed data will provide high-quality input for the deep learning model, significantly improving the effect of model training and further enhancing the evaluation accuracy of ablation effects.

[0075] Step 3: Construction of a deep learning model, specifically including:

[0076] Model selection:

[0077] The U-Net structure is adopted, which has good image segmentation performance;

[0078] U-Net is a convolutional neural network (CNN) specifically designed for biomedical image segmentation. Its structure consists of an encoder and a decoder. The encoder is responsible for extracting features, and the decoder is used to restore the spatial information of the image, enabling accurate pixel-level segmentation. U-Net performs excellently in medical image segmentation and is particularly suitable for processing tumor regions with complex shapes and boundaries. Its "skip connection" design combines high-level features with low-level features, effectively retaining detailed information and avoiding information loss, thereby improving the segmentation accuracy. U-Net has high training efficiency and is suitable for small-sample learning, which is particularly important for the usually small-sized medical image datasets.

[0079] Model training:

[0080] Use the processed multi-modal dataset for model training, label the tumor region as the training target, and adopt cross-validation technology to optimize model parameters to ensure the generalization ability of the model.

[0081] Use a pre - processed multi - modal dataset to ensure data quality and consistency. Precisely annotate the tumor region to generate corresponding segmentation masks as the targets for model training. Build a U - Net model using deep learning frameworks such as TensorFlow or PyTorch, initialize the model parameters, and load the pre - processed dataset. Set a loss function such as the cross - entropy loss function to evaluate the segmentation performance of the model, and optimize the network parameters. Adopt the k - fold cross - validation method, divide the training data into k parts, and conduct k times of training and validation to improve the generalization ability of the model. During each training, use k - 1 parts of the data for training and the remaining part for validation. Finally, comprehensively integrate the results of each validation and select the model parameters with the best performance. Adjust hyperparameters such as the learning rate, batch size, and number of iterations to ensure the convergence and stability of the model during training. Use appropriate evaluation metrics such as the Dice coefficient to evaluate the performance of the model on the validation set, and analyze the segmentation effects of the model on different types of tumors and different - modality images to ensure adaptability to various situations.

[0082] By using the U - Net model, the system can achieve higher accuracy in tumor image segmentation, thus providing more reliable basic data for the evaluation of ablation effects. Accurate segmentation of the tumor region can help doctors more clearly identify the scope and characteristics of the tumor, thereby formulating more reasonable treatment plans and improving the effect of personalized treatment.

[0083] Step Four: Real - time image data fusion, specifically including:

[0084] Implementation of the image fusion algorithm:

[0085] Implement a deep - learning - based real - time image fusion algorithm to synthesize image data from CT, MRI, and ultrasound to generate high - resolution composite images;

[0086] Adopt a deep - learning - based image fusion model, such as using a convolutional neural network (CNN) or a generative adversarial network (GAN) to achieve the fusion of CT, MRI, and ultrasound images. Design a specific network architecture so that it can learn the correlation features between different modalities, thereby generating high - quality composite images. Use a deep network to extract the features of CT, MRI, and ultrasound images, and combine these features through a specific fusion layer to generate composite images. Adopt strategies such as weighted fusion, feature - level fusion, or decision - level fusion. According to the characteristics and importance of different modalities, determine the contribution of each modality to the final image. Use the annotated multi - modal dataset to train the fusion model to ensure that it can accurately capture the characteristic information of the tumor and generate high - resolution composite images. Optimize the model parameters and verify the fusion effect through cross - validation and performance evaluation using metrics such as PSNR and SSIM.

[0087] Real - time feedback mechanism:

[0088] During the ablation process, the tumor status is monitored in real time, and the system automatically updates and displays the latest fused images, providing real-time feedback to the operating doctor.

[0089] During the ablation operation, the system receives real-time image data from CT, MRI, and ultrasonic devices to ensure obtaining the latest tumor status information. The system uses a trained image fusion model to automatically perform fusion processing every time new image data is input, generating the latest composite image. The generated fused image can accurately reflect the current status of the tumor and is updated and displayed in real time on the monitor of the operating doctor;

[0090] The high-quality composite images obtained in real time can provide doctors with more comprehensive tumor information, helping them formulate more precise ablation plans, improve the treatment effect. Traditional image fusion methods usually rely on simple image superposition or image stitching and lack the feature extraction ability of deep learning. The fusion algorithm based on deep learning can effectively capture the key information of different modality images and generate composite images with higher resolution and higher contrast. Existing technologies often have delays in real-time updates, affecting the decision-making efficiency of doctors. However, this solution can instantaneously provide the latest tumor status information during the ablation process through a real-time fusion algorithm and an image update mechanism, enhancing the flexibility and timeliness of the operation, monitoring the tumor status in real time and updating the fused images, providing instant feedback to doctors, enabling them to promptly detect potential problems and adjust treatment strategies in a timely manner, reducing the risk of complications. Through automated real-time image fusion, the information acquisition process for doctors during the operation is simplified, the time for manual switching and adjustment is reduced, and the overall fluency and efficiency of the operation are improved.

[0091] Step Five: Monitoring and Optimization during the Ablation Process, specifically including:

[0092] Dynamic Monitoring:

[0093] Configure a real-time monitoring system and, in combination with biological signals, monitor the patient's status at any time during the ablation process;

[0094] Configure advanced vital sign monitoring devices, including a heart rate monitor, a sphygmomanometer, a blood oxygen saturation meter, etc., to monitor the patient's physiological status in real time. In combination with the image monitoring system, ensure that biological signals and image data can be obtained simultaneously during the ablation process, monitor heart rate, blood pressure, respiratory rate, blood oxygen saturation, etc., promptly identify the patient's physiological changes, guard against potential risks, set key thresholds, and once the physiological parameters exceed the normal range, the system will issue an alarm to alert the doctor, update the fused images in real time, and display the ablation status of the tumor, enabling the doctor to accurately judge the ablation progress and effect.

[0095] Precisely Adjust Ablation Parameters:

[0096] Dynamically adjust the power and time of the ablation device according to the fused image and biological signals in real-time feedback to ensure the efficiency and safety of the ablation process.

[0097] Utilize the biologically-sensed signals and fused images monitored in real-time for data analysis to evaluate the ablation effect and the patient's status, respond promptly to changes, set up a calculation model, calculate the optimal ablation parameters such as power and time based on the real-time data of the image feedback and biological signals, and transmit the calculated ablation parameters to the ablation device through an automated interface to achieve real-time control. The doctor can manually adjust or confirm parameter modifications to ensure the best ablation method is adopted in specific situations. Dynamically customize the ablation plan according to the individual differences of the patient (such as physiological parameters, tumor type, etc.) to ensure the individuation and precision of the treatment. Continuously optimize the ablation process according to real-time feedback to enhance the effect and reduce damage to surrounding healthy tissues.

[0098] The implementation of the dynamic monitoring system can promptly detect the abnormal status of the patient, reduce the risk of complications, and ensure the safe progress of the ablation process. Existing technologies often lack comprehensive monitoring of the patient's physiological state, which may lead to the inability to timely identify risks during the ablation process. Through dynamic monitoring, the physiological state of the patient can be grasped in real-time, and the ablation strategy can be adjusted promptly, significantly enhancing the safety of the treatment. The parameter settings in traditional ablation methods are usually preset and do not take into account the changes of the patient during the operation. This solution enables dynamic adjustment through real-time data feedback, making the ablation process more efficient and precise, significantly improving the ablation effect. The ability to adjust ablation parameters in real-time makes the treatment process more flexible, ensuring the effectiveness and efficiency of ablation, thereby increasing the overall treatment success rate.

[0099] Step Six: Effect evaluation and feedback, specifically including:

[0100] Ablation effect evaluation:

[0101] After ablation, use the fused image for effect evaluation, identify the ablation area and tumor residue, and quantify the ablation effect using an algorithm.

[0102] Analyze the fused image after ablation using a trained deep learning model to accurately identify the ablation area and the remaining part of the tumor. The model automatically detects changes in the tumor by comparing the images before and after ablation, extracts key features such as ablation area and tumor volume change, uses image analysis algorithms (such as threshold segmentation, edge detection, etc.) to quantify the ablation effect, calculates indicators such as ablation success rate and tumor residue ratio, compares the image data before and after ablation, generates relevant statistical data, provides a scientific basis for subsequent treatment, combines the physiological feedback data (such as heart rate, blood pressure, etc.) of the patient after ablation with the image analysis results, comprehensively evaluates the ablation effect and the overall condition of the patient, and through comprehensive algorithm analysis, obtains the impact of the ablation effect on the patient's physiological state to provide a more in-depth evaluation.

[0103] Generate a report:

[0104] Automatically generate a detailed ablation effect report, including the ablation area, tumor changes, and patient physiological feedback data, for reference in subsequent medical decisions.

[0105] The ablation effect report will include a detailed description of the ablation area, tumor changes, patient physiological feedback data, quantitative ablation effect indicators, etc. At the same time, the report can include comparison images to show the changes before and after ablation, so as to facilitate doctors and patients to intuitively understand the treatment effect. Traditional methods usually rely on manual recording and report generation, which are prone to errors and omissions. The automatic report generation mechanism of this solution not only improves efficiency but also reduces the risk of manual intervention, ensuring the accuracy and consistency of the report;

[0106] The standardization of ablation effect evaluation and report generation helps to establish a treatment case database, promote the accumulation and summary of clinical experience, improve the standardization and safety of subsequent treatments. Through the quantitative ablation effect and physiological feedback data, doctors can more deeply understand the treatment results and make more scientific subsequent treatment decisions, improving the overall medical quality.

[0107] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A precise tumor interventional ablation evaluation feedback system, characterized in that: The steps include: Step 1: System hardware configuration; Step 2: Data collection and preprocessing; Step 3: Deep learning model construction; Step 4: Real-time image data fusion; Step 5: Ablation process monitoring and optimization; Step 6: Effect evaluation and feedback.

2. The precise tumor interventional ablation evaluation feedback system according to claim 1, characterized in that: The step 1: system hardware configuration, specifically includes: Equipment selection: Select high-resolution CT and MRI equipment to ensure that they can provide high-quality imaging data, and equip them with high-frequency ultrasound equipment to facilitate real-time monitoring of tumor ablation effects; Computing platform construction: Configure high-performance GPU servers to ensure that deep learning algorithms can run efficiently during real-time processing and that the system has sufficient storage capacity to store large amounts of image data and training models.

3. The precise tumor interventional ablation evaluation feedback system according to claim 2, characterized in that: The step 2: data collection and preprocessing, specifically includes: Initial data collection: Before surgery, multimodal imaging data collection using CT, MRI, and ultrasound should be performed. The collected data should include the location information and morphological characteristics of the tumor. Data preprocessing: Clean and standardize data, including image denoising, alignment and resampling, so that data from different image sources can be processed under the same standard, and use image enhancement technology to improve image quality and ensure effectiveness during subsequent processing.

4. The precise tumor interventional ablation evaluation feedback system according to claim 3, characterized in that: The step three: deep learning model construction, specifically includes: Model selection: Adopting U-Net structure, it has good image segmentation performance; Model training: The processed multimodal dataset was used for model training, the tumor area was annotated as the training target, and cross-validation technology was used to optimize the model parameters to ensure the generalization ability of the model.

5. The precise tumor interventional ablation evaluation feedback system according to claim 1, characterized in that: The step 4: real-time image data fusion, specifically includes: Image fusion algorithm implementation: Implement a real-time image fusion algorithm based on deep learning to synthesize image data from CT, MRI and ultrasound to generate high-resolution composite images; Real-time feedback mechanism: During the ablation process, the tumor status is monitored in real time, and the system automatically updates and displays the latest fusion images, providing real-time feedback to the operating physician.

6. The precise tumor interventional ablation evaluation feedback system according to claim 1, characterized in that: The step 5: ablation process monitoring and optimization, specifically includes: Dynamic monitoring: Configure a real-time monitoring system that combines biological signals to monitor the patient's status at any time during the ablation process; Precisely adjust ablation parameters: Based on the real-time feedback of fused images and biological signals, the power and time of the ablation device are dynamically adjusted to ensure that the ablation process is efficient and safe.

7. The precise tumor interventional ablation evaluation feedback system according to claim 1, characterized in that: The step six: effect evaluation and feedback, specifically includes: Ablation effect evaluation: After the ablation is completed, the fused images are used to evaluate the effect, identify the ablation area and tumor residue, and use algorithms to quantify the ablation effect; Generate a report: Automatically generate detailed ablation effect reports, including ablation area, tumor changes, and patient physiological feedback data, for reference in subsequent medical decision-making.

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