502 results about "Brain tumor" patented technology

The invention discloses a near-infrared emission aggregation-induced emission organic photo-thermal molecule and a preparation method and application thereof, and belongs to the technical field of biomedicine, and the structure is shown as a formula I: # imgabs0 # (I), wherein R1, R2, R3, R4, R5 and R6 are independently selected from groups containing alkyl chains. The invention also provides a preparation method of the organic photo-thermal molecule and application of the organic photo-thermal molecule in preparation of multifunctional protein nanoparticles with near-infrared aggregation-induced emission. The near-infrared emission aggregation-induced emission organic photo-thermal molecule provided by the invention shows excellent near-infrared imaging effect and photo-thermal capability under laser irradiation, and the obtained near-infrared aggregation-induced emission multifunctional protein nano particle has excellent photo-thermal effect and imaging resolution, has excellent targeting to brain tumor, and has good application prospects in blood brain barrier crossing, brain tumor targeting and brain tumor targeting. And tumor killing, photothermal and ferroptosis combined treatment has practical medical significance, and is expected to be widely applied to the aspects of intelligent drug delivery and nano medical treatment.

The present invention provides methods, devices, and systems for in vivo treatment of cell proliferative disorders. Included is a method of treating tissue with electrical energy, the method comprising: delivering electrical energy to tissue using one or more electroporation devices comprising one or more electrodes; and cooling the tissue, surrounding tissue, one or more of the electrodes, or one or more of the electroporation devices to minimize heating. In embodiments, the invention can be used to treat solid tumors, such as brain tumors, and in some embodiments, exemplary methods rely on non-thermal irreversible electroporation (IRE) to cause cell death in treated tumors.

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention discloses a brain tumor MRI image detection method based on improved YOLOv8n, and belongs to the crossing field of computer science and technology and medical image.The method comprises the steps that a brain tumor MRI image to be detected is obtained, and the brain tumor MRI image to be detected is input into a brain tumor MRI image detection network model to obtain a brain tumor detection result; the brain tumor MRI image detection network model adopts an improved YOLOv8n model; according to the improved YOLOv8n model, a lightweight backbone network improved based on StarNet is used as a backbone network of the model on the basis of an original YOLOv8n model, a simple and parameter-free attention module SimAM is introduced behind an SPPF layer, an improved C2fStar module is used for replacing a C2f module in a neck network, and the improved YOLOv8n model has higher efficiency and better adaptability while high precision is kept; especially in an environment where computing resources are limited.

The invention provides an MRI brain tumor image segmentation method based on a state space model and a frequency domain, and relates to the field of image processing. The method comprises the following steps: acquiring a brain tumor MRI image to be segmented and preprocessing the brain tumor MRI image; and inputting the preprocessed brain tumor MRI image into a segmentation network based on a state space model and a frequency domain. The network is based on a U-Net architecture, and global context modeling is performed on feature information by introducing a state space model into a bottleneck layer. In order to optimize feature extraction, a dynamic weight mechanism is designed aiming at features input into a state space model, the importance of feature sequences in three directions is dynamically adjusted according to input data, and the attention to a brain tumor key area is enhanced. By adding a 3D frequency domain fusion module in jump connection, high-frequency noise is reduced, and perception of the overall structure of a brain tumor image is enhanced. According to the method, the state space model and the 3D frequency domain fusion module are introduced, and the importance of the feature sequence is dynamically adjusted, so that brain tumor MRI image segmentation with higher precision is realized.

The invention provides a brain tumor image accelerated diffusion network segmentation method based on fuzzy learning, and belongs to the technical field of medical image intelligent segmentation. The technical problem of low segmentation accuracy caused by fuzzy texture and boundary disappearance of the brain tumor image is solved. According to the technical scheme, the method comprises the following steps of S1, performing data preprocessing on a brain tumor image data set; s2, performing pre-segmentation on the brain tumor image by using a 3D U-Net network, and performing iterative noise addition on the obtained segmented image to enable the segmented image to become a pure noise image; s3, performing iterative denoising on the pure noise image, and learning a denoising process by using a fuzzy U-Net network; and S4, stopping the denoising process in advance, and segmenting the segmented image by using a 3D U-Net network to obtain a final segmentation result. The method has the advantages that the segmentation accuracy is high, decision support is provided for segmentation of the brain tumor image, and convenience is provided for diagnosis of doctors.

The invention discloses a brain tumor segmentation method based on space attention and edge recognition enhancement, and the method comprises the steps: inputting an MRI brain tumor image into a trained brain tumor segmentation model, and outputting a corresponding prediction brain tumor segmentation mask; the brain tumor segmentation model is constructed based on a U-net network and comprises an encoder composed of a plurality of lower convolution modules, a decoder composed of a plurality of upper convolution modules in one-to-one correspondence with the lower convolution modules of the encoder, and an edge attention module arranged at the bottommost layer of the U-net network. The output of each lower convolution module in the encoder is in jump connection with the input of the corresponding upper convolution module in the decoder, and a space attention module is arranged in each jump connection. According to the method, the capability of capturing long-distance dependence and context information of the whole image of the network is improved through the space attention module, and complete edge feature information is provided for the feature map through the edge attention module, so that the accuracy of brain tumor segmentation is improved.

The invention discloses a brain tumor segmentation method and system based on a diffusion model, and relates to the field of medical images and deep learning. The method comprises the following steps: acquiring a disclosed three-dimensional brain medical image data set, and making a two-dimensional brain medical image through data processing; performing data preprocessing and data enhancement operation on the two-dimensional image, and dividing the two-dimensional image into a training set, a verification set and a test set according to a certain proportion; a DiffIRseg network model is built, a two-stage training strategy is adopted for training, and optimal model weight parameters are stored; and inputting a to-be-segmented brain image to the trained DiffIRseg network, outputting a predicted health image, obtaining a brain tumor segmentation result through difference analysis, and comparing the brain tumor segmentation result with the fine annotation for verification. According to the method, the prior knowledge of the health image and the denoising characteristic of the diffusion model are introduced, so that the labeling cost and complexity are reduced, the accuracy and efficiency of brain tumor segmentation are improved, and reliable technical support is provided for clinical diagnosis and treatment planning.

The invention relates to the field of brain tumor analysis, and discloses a brain tumor image analysis system based on artificial intelligence, comprising: a spatial alignment unit for acquiring original image data of the brain of a subject; performing spatial alignment on the original image data according to a cross-modal registration algorithm to obtain standardized image data; the feature extraction unit is used for performing tumor region initial segmentation on the standardized image data according to a three-dimensional convolutional neural network so as to obtain a coarse segmentation probability graph; and extracting three-dimensional geometric feature parameters of the tumor candidate region according to the coarse segmentation probability graph. According to the method, the original image data is spatially aligned through the cross-modal registration algorithm, and the spatial consistency between different image sources is ensured, so that the image data under different modals can be accurately compared and analyzed, and an accurate spatial reference is provided for subsequent tumor region identification and processing.

The invention discloses a brain MRI image segmentation method based on an RWKV model, and belongs to the technical field of medical image processing and artificial intelligence crossing. Firstly, an RWKV linear self-attention module is introduced into a U-Net network framework, and association between remote pixels is established with relatively low calculation overhead, so that the recognition precision of a brain tumor area is improved, the reasoning time is effectively controlled, and the practicability of a model is enhanced. Secondly, according to the method, multi-scale coding and a feature fusion mechanism are combined, local and global information is extracted in a combined manner, features are mined from different resolution levels, and adaptive fusion is realized, so that the fine-grained segmentation effect is improved. And finally, in order to enhance the generalization ability and lightweight deployment performance of the model, the network structure is further optimized, and the overall computing resource demand is reduced, so that the method can better adapt to cross-patient MRI data in a complex clinical environment, and has good practical value and popularization potential.

The invention relates to the technical field of brain surgery, in particular to a construction method based on a craniocerebral tumor minimally invasive surgery risk assessment model. The method comprises the following steps: acquiring craniocerebral tumor minimally invasive surgery clinical data, including patient iconography data, surgery record data, postoperative complication data and patient basic data; performing data cleaning on the surgical clinical data, and performing standardization processing to unify a data format so as to obtain a standardized clinical data set; and extracting the size, position and shape of the tumor and the relationship between the tumor and surrounding tissues from the standardized clinical data set, and generating a tumor space structure model by using a three-dimensional reconstruction technology. According to the method, main factors influencing the surgical risk are identified through a statistical analysis method, including the contribution degree of each feature to the surgical risk and the interaction between the features, and it is ensured that the evaluation process is more objective and accurate.

The invention relates to the technical field of medical data analysis, in particular to a brain tumor curative effect analysis system which comprises a tumor data sensing layer for collecting multi-department diagnosis and treatment data, tumor image data and patient pathology monitoring data; the tumor feature processing center extracts data features, correlates data and core features before and after treatment through an attention mechanism, and generates a tumor complete-cycle unified feature map; the therapeutic effect dynamic analysis unit evaluates the therapeutic effect in stages, and outputs a therapeutic effect index and a recurrence risk value through a self-supervised model; the dynamic adaptation decision module is used for generating personalized treatment adjustment suggestions based on the dynamic change of the blood brain barrier in combination with the curative effect index, the recurrence risk value and the multi-omics characteristics of the patient; and the AI multi-department consultation unit automatically matches similar cases with field expert suggestions, and formulates a target diagnosis and treatment scheme based on a visual platform and multi-department doctor collaborative consultation in combination with personalized treatment adjustment suggestions. Therefore, the problems of lagging effect evaluation, insufficient diagnosis and treatment suggestions and the like in the prior art are solved.

The invention discloses a cascade multi-scale convolution and modal enhancement brain tumor segmentation method based on a Mama framework, and relates to the technical field of brain tumors. According to the method, an MCME-UNet model integrated with an MCMS module is provided, through a hierarchical feature extraction mechanism and a multi-modal feature fusion strategy optimized by an MEM module, the segmentation precision is remarkably improved while the calculation efficiency is kept, and particularly, an EEM module innovatively applies Sobel operator to be connected with residual errors, so that the tumor boundary continuity and the segmentation accuracy are synchronously improved; moreover, a novel training normal form of a focus Tversky loss function is introduced, so that the problem of class imbalance is effectively solved, the sensitivity of the model to a small tumor region is enhanced, a smoother segmentation boundary can be generated, and the stability and accuracy of a segmentation result are greatly improved.

The invention relates to the technical field of image segmentation, in particular to a missing modal brain tumor image segmentation method based on a multi-axis shift MLP modal mask, which comprises the following steps: constructing a brain tumor image segmentation model which comprises four encoders and a shared decoder, a multi-level cross-modal interaction module and a space weight attention module are introduced between the encoder and the shared encoder; respectively inputting the brain tumor images of the four modalities into corresponding encoders, and outputting encoding features of different levels and different resolutions of each modal image; the multi-level cross-modal interaction module captures complementary features among modals in combination with global context information of the coding features; and the spatial weight attention module re-weights the four modal features from different angles, and inputs the four modal features into a shared decoder for decoding to obtain a segmented image. According to the method, the missing-mode brain tumor image segmentation can be more accurately realized.

The invention belongs to the technical field of medical image analysis, and relates to a brain tumor segmentation method based on uncertainty estimation, and the method comprises the steps: inputting T1, T1c, T2 and Flair images of a brain tumor into a trained image segmentation model which comprises four encoder modules, one decoder module, one Monte Carlo simulation module and one image processing module; the Monte Carlo module repeatedly calls the encoder module and the decoder module for sampling; and the image processing module processes the output to obtain a mean value image, a random uncertainty image, a cognitive uncertainty image, an entropy uncertainty image and a mutual information uncertainty image, thereby realizing accurate segmentation and analysis of the brain tumor. The method improves the segmentation accuracy and the diagnosis reliability, is high in expansibility, and is suitable for various medical image analysis tasks.

The invention discloses a multi-scale brain tumor segmentation method and system based on adaptive KAN, and a storage medium. The method comprises the following steps: preprocessing three-dimensional brain magnetic resonance imaging data; the preprocessed data are input into an encoder, the encoder comprises a plurality of levels, and each level executes convolution operation to extract local features, executes spatial KAN processing to extract spatial features and downsamples a feature map; the output of the encoder is input into a bottleneck layer, and the bottleneck layer captures a multi-scale global context by using a plurality of parallel expansion convolution branches; the output of the bottleneck layer is input into a decoder, the decoder comprises a plurality of stages, and each stage executes transposing a convolution up-sampling feature map, executes cross-scale gating processing to fuse encoder jump connection features and decoder features, and executes spatial KAN processing to optimize features; and the output of the decoder is input into the output module. According to the method, the problems of low calculation efficiency, poor tumor heterogeneity adaptation, insufficient multi-scale context capture and the like in the existing brain tumor segmentation can be effectively solved.

The invention provides a missing-modal brain tumor image segmentation method, device, equipment, medium and product, and belongs to the technical field of image segmentation, a knowledge distillation model architecture is constructed, the knowledge distillation model architecture comprises a teacher network and a student network, the student network comprises at least one feature reconstruction module, and the teacher network comprises at least one feature reconstruction module; the feature reconstruction module is used for reconstructing brain tumor image features of a missing mode based on semantic relevance between brain tumor images of different modes; the student network is trained based on the trained teacher network, an image segmentation model is obtained according to the trained student network, and the image segmentation model is used for performing image segmentation on the to-be-segmented missing modal brain tumor image. The student network can reconstruct the brain tumor image features of the missing mode based on the semantic relevance between the brain tumor images of different modes, can better represent the brain tumor image features of the missing mode, and effectively improves the segmentation accuracy of the brain tumor images of the missing mode.

The invention discloses a brain tumor region segmentation method based on adaptive boundary guided aggregation SAM, and relates to the field of medical image analysis. The fusion output of the multi-modal magnetic resonance image is input into an image segmentation model to obtain image embedding, the boundary embedding of the brain tumor and the real boundary of the tumor are obtained through a boundary mining network, the boundary features are enhanced through an adaptive boundary enhancement model, the image embedding and the boundary embedding are output as fusion embedding by applying dynamic fusion, and the real boundary of the tumor is obtained. Inputting the fused embedded and adaptive enhanced boundary features and the features of the prompt encoder into a mask decoder of a segmentation model to obtain a segmentation result, and obtaining a total loss function in combination with a boundary mining network and a loss function based on region prediction; according to the brain tumor region segmentation method based on adaptive boundary guided aggregation SAM, accurate segmentation of the brain tumor region is realized.

The invention provides a brain tumor classification method based on deep learning, and the method comprises the steps: uniformly segmenting a multi-mode magnetic resonance imaging image into a plurality of small blocks, generating an enhanced puzzle through random geometric transformation, inputting the enhanced puzzle into a ResNet50 backbone network, extracting primary local features, and optimizing feature distribution; multi-scale feature maps of different stages are obtained, lightweight convolution blocks are constructed, and local pathological relevance is enhanced; performing full-connection layer splicing on the multi-scale feature maps in different stages, introducing a learnable weight matrix to dynamically allocate weights in each stage, and generating fusion features; a progressive parameter unfreezing mechanism is adopted to ensure orderly learning of the model from local to global; the sample weight is dynamically adjusted according to the category frequency, and the local discriminant force and the global consistency are balanced in combination with multi-stage classifier loss weighted fusion; and a final classification result is generated through multi-stage prediction probability weighted average and dynamic threshold adjustment, and clinical availability is improved in combination with a sigmoid calibration module.

The invention relates to the technical field of medical image processing, in particular to a nuclear magnetic image brain tumor detection method and system, and the method comprises the steps: obtaining an initial nuclear magnetic brain tumor image, taking a dual-convolution cross-stage division network as a backbone network, taking a partial visual state space network as a feature enhancement network, lightening a YOLOv8 model, and improving the performance at the same time; in the YOLOv8 model with improved performance, the constructed re-parameterized dynamic selectable large-kernel convolutional network is used as a multi-scale feature interaction network in the neck structure, and the performance of the YOLOv8 model is further improved by using re-parameterization, so that the improved YOLOv8 model is obtained; performing iterative optimization training on the improved YOLOv8 model by using the initial image data set to obtain a nuclear magnetic image brain tumor detection model; and inputting the initial image data set into a nuclear magnetic image brain tumor detection model for detection, and outputting a nuclear magnetic brain tumor detection result. According to the invention, the accuracy and efficiency of brain tumor detection can be improved.

The invention relates to the technical field of deep learning, and discloses a brain tumor feature extraction method and system of a multi-scale dynamic calibration detection network based on comparative learning, and the method comprises the steps: inputting a brain tumor image into a self-supervised comparative learning network, extracting key features through self-supervised comparative learning, and outputting a multi-scale feature map; performing down-sampling and up-sampling combined operation on the multi-scale feature map, inputting the multi-scale feature map into a multi-scale adaptive network for dynamic feature calibration, and optimizing multi-scale boundary features through dynamic sampling and shape self-calibration; and the output of the multi-scale adaptive network for dynamic feature calibration is input into the multi-domain attention network for dynamic deformable convolution, and key feature focusing is enhanced through residual connection and dynamic deformable convolution. And the accuracy and reliability of brain tumor feature extraction are improved.

The invention discloses a multi-scale lightweight brain tumor segmentation method based on improved YOLOv8n-Seg, belongs to the technical field of medical image processor artificial intelligence, and designs a novel down-sampling module FLRDown fusing low-rank convolution and Fourier transform to replace standard convolution operation in a YOLOv8n-Seg backbone network. Further improvement is carried out on the basis of a SimAM attention mechanism, and a novel attention module-AdaSimAM is provided; on the basis of an original YOLOv8n-Seg segmentation header, an LCSDSH segmentation header network is designed, the structure of the header network is optimized, and redundant parameters are reduced. According to the method, a structure optimization and feature enhancement mechanism is introduced, the calculation complexity of the model is effectively reduced, meanwhile, the perception ability for a small target area is enhanced, the robustness and generalization ability of the model in multi-scale tumor recognition are improved, and therefore the method is more suitable for the actual requirement for automatic segmentation of the brain tumor in clinical practice.

In various aspects, the present invention provides a composition having orthogonally tunable elasticity and viscosity. In some embodiments, the composition comprises an interpenetrating polymer network comprising viscous and elastic polymer networks that are not covalently bonded. In some embodiments, the composition is a viscoelastic hydrogel. In some embodiments, the composition mimics the viscoelasticity of at least one organ, organoid, or tissue of interest (e.g., brain, pancreas, brain tumor, etc.). In various aspects, the present invention provides a device (e.g., 3-dimensional cell culture platform) comprising said composition. In some aspects, the present invention provides a method of preparing said devices. In some embodiments, the method comprises bioprinting.

The invention relates to the related technical field of primary cell (PDC) culture, in particular to a method for culturing primary cells (PDC) from glioma patients, which comprises a series of steps of Swis3T3-J2 cell culture, conditioned medium preparation, brain tumor tissue pretreatment, primary tumor cell digestion and culture and the like. Primary human tumor cells are cultured by a conditioned culture medium generated by fibroblasts irradiated by X-rays (X) and a cobalt-60 radioactive source (60Co), and culture conditions of key features of parent tumors and a modeling process can be most effectively reserved by screening and optimizing the culture conditions. And whether the PDC retains the key characteristics of the parent tumor or not is inspected on the aspects of the cell molecular level, the cell phenotype, the animal intracranial tumor model and the like.

The invention discloses a brain tumor detection method based on attention mechanism and MRI (Magnetic Resonance Imaging) multi-modal fusion, and relates to the technical field of brain tumor image analysis. Calculating a consistency measurement score of the two heat maps, and if the score does not meet a preset threshold value, starting feedback iteration: merging the images to generate an enhanced training set, performing weighted training on a loss item corresponding to the multi-modal image by taking the normalized measurement score as a quality weight, and iteratively optimizing the model until a condition is met; and finally, outputting a brain tumor detection result with high confidence. According to the invention, by introducing an internal closed-loop verification and adaptive optimization mechanism, the core problem that the confidence of the detection result cannot be self-verified and guaranteed in the prior art is effectively solved. And finally, the accuracy of brain tumor detection confidence closed-loop verification optimization based on attention mechanism and MRI multi-mode fusion is improved.

The invention discloses a multi-mode brain tumor robust segmentation method based on graph-guided adaptive distillation, and the method comprises the steps: firstly constructing a brain tumor segmentation model which comprises a graph-guided adaptive refining module GARM module, a double-bottleneck distillation module BBDM module and a lesion perception reliability module LGRM module; secondly, acquiring a multi-modal brain medical image, executing standardization preprocessing, and then dividing a training set and a test set according to a proportion; and finally, inputting the training set into the brain tumor segmentation model to obtain a segmentation result graph for training, and performing evaluation through the test set. According to the method, the student model can still have higher adaptability and robustness under the condition of lack of modals, the effectiveness and generalization ability of knowledge distillation are greatly improved, and accurate and efficient brain image segmentation is realized.

The invention relates to the technical field of image segmentation, and provides a brain tumor segmentation method based on multi-modal fusion. The method comprises the following steps: 1, obtaining a multi-modal brain tumor image, and preprocessing the multi-modal brain tumor image to construct a multi-modal brain tumor data set; step 2, constructing a brain tumor segmentation network structure, including: selecting a Unet network as a reference model, and adding a multi-scale progressive fusion MFCM module at a jump joint of each coding layer and each decoding layer in the Unet network; 3, training the brain tumor segmentation network structure by using the multi-modal brain tumor data set to obtain a brain tumor segmentation model; and 4, preprocessing a to-be-detected multi-modal brain tumor image, and inputting the preprocessed to-be-detected multi-modal brain tumor image into the brain tumor segmentation model to obtain a segmentation result. According to the method, the segmentation performance of the model can be improved while the multi-modal features are fully utilized.

The invention discloses a multi-axis RWKV-UNet + + multi-mode MRI (Magnetic Resonance Imaging) brain tumor segmentation method, and belongs to the technical field of medical image processing. According to the invention, multi-modal MRI three-dimensional body data is input and preprocessed, and fusion features are output through a modal fusion module; the fusion features are input into an encoder containing multi-axis RWKV sequence modeling, and long-range dependence is extracted; after the output of the encoder is processed by the bottleneck layer, the global Token aggregator converges the global context and reinjects the global context; the enhanced features are input into a UNet + + nested topology decoder, the jump features are fused with the up-sampling features after being subjected to jump RWKV semantic alignment, and finally a three-dimensional segmentation probability graph is generated through mapping. The method is mainly used for accurate three-dimensional segmentation of the multi-mode MRI brain tumor, and provides support for clinical brain tumor diagnosis and treatment.

The invention provides a three-dimensional MRI (Magnetic Resonance Imaging) brain tumor segmentation method based on a diffusion model, and relates to the technical field of medical image generation, and the method comprises the steps: collecting MRI images of different modes of a brain tumor part and corresponding segmentation reference true values; performing noise addition on the segmentation reference true value through forward diffusion to obtain noise-added segmentation data; performing iterative discrete wavelet transform on the MRI image to obtain wavelet information of each level; constructing a brain tumor segmentation model for improving a diffusion model on the basis of introducing a cross attention mechanism into a U-Net network structure, and training the brain tumor segmentation model by using the data; a random noise is sampled from prior distribution, the random noise and a to-be-segmented MRI image are spliced and then input into the trained model, a target domain segmentation image corresponding to reasoning is obtained, the method can extract multi-scale features of multi-modal MRI, the parameter quantity of the model is remarkably reduced, condition information is fused into a network, feature expression of a key area is enhanced, and the target domain segmentation image is obtained. Therefore, the accuracy of MRI brain tumor segmentation is improved.

The invention provides an MRI (Magnetic Resonance Imaging) missing mode generation method based on a learnable frequency domain module, which is used for reconstructing and synthesizing any missing mode of a brain glioma medical multi-mode image. The method comprises the steps that a training set and a test set of brain tumor multi-mode MRI images are acquired, the training set and the test set comprise a plurality of samples, each sample comprises four original mode images, and each original mode image is an available mode or a missing mode; a missing modal brain tumor generation network model is constructed, and the missing modal brain tumor generation network model comprises a learnable frequency domain module and a condition diffusion module based on modal attention; inputting the training set into the model for training, setting a joint loss function, and optimizing parameters of the model through the joint loss function of each sample to obtain an optimized model; and inputting the multi-mode MRI image in the test set into the optimized model to obtain a generated image corresponding to the missing mode.